The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology.In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models.The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions. The initial concept of artificial intelligence (AI) was first coined as far back as 1956 –4. AlexNIn recent years, many studies have applied AI algorithms to the most modern retinal imaging modalities, including optical coherence tomography, to detect or quantify an array of retinal features of interest  –23. AI-eSuch AI-enabled technologies have potential to be implemented into clinical practice in several ways. The use of AI technologies to screen or classify retinal diseases may play a role in telemedicine. They may also assist healthcare providers with greater speed, repeatability, reproducibility, and consistency than human graders. Uniting clinicians with AI systems has been proven to be synergistic, achieving better performance than either alone . TherefoAcademic institutions and technology companies (e.g. Google) increasingly engage in AI research and boost their investment and involvement in this field . FurtherThe key issues for deploying AI technologies in telemedicine or healthcare systems may have a profound and lasting influence on near-future practice in ophthalmology.In this article, we summarize four key requirements surrounding the application and execution of AI-enabled technology for diagnosis and screening in retinal diseases in real-world practice. Informing and operationalizing an AI healthcare system includes processing large data sets, practicability in ophthalmology, policy compliance and regulatory environment, and balancing profit and cost in adopting AI-enabled technologies. Data processing is crucial before developing an AI model. It includes data standardization, data sharing system, and the maintenance of data privacy in the infrastructure of AI systems.The high dependency of modern ophthalmology on imaging makes it an attractive field for the development of AI models. However, the diversity of proprietary devices, image acquisition, and data storage processes poses a barrier to research teams. The need for data standardization has become pivotal, not only for expanding the scale of AI models but for providing more effective ways to achieve clinical benefits. In 2021, the American Academy of Ophthalmology suggested that manufacturers of ophthalmic devices should standardize the format of digital images, integration of medical data, and picture archiving to comply with the 12 Digital Imaging and Communications in Medicine standards, developed by the American Academy of Ophthalmology in collaboration with manufacturers . Such stAs of 2021, nearly 94 ophthalmic data sets containing more than 500 000 images were openly accessible . The traThe shared data should be deidentified and anonymized to comply with privacy and cybersecurity frameworks. Data privacy poses challenges in technological, legal, and ethical fields. It can be difficult to precisely define data privacy because traditional deidentification is vulnerable to linkage attacks from intended third parties . MoreoveData privacy may also be addressed by collating all relevant data into trusted research environments or data decentralization. In federated analysis, the algorithmic code is sent to each data site for individual analysis; then the results are brought back to the central site for aggregation and further analysis . RecentlIn 2021, the American Academy of Ophthalmology Committee on Artificial Intelligence raised three ethical concerns: transparency, meaning the adequate explanation or interpretation of the AI model; responsibility, which addresses moral or legal concerns; and scalability of implementation of AI models, which depends on equality of data distribution and potential systemic bias in AI models ▪. AI tecAs the field of ophthalmic AI evolves, the quality of reporting results from different AI systems may be discrepant and incomprehensive. It is necessary to have consensus in determining adequate description, translation, and appraisal of ophthalmic AI research to ensure robust algorithms and generalizability into real-world settings.▪▪]. Subsequently, other reporting guidelines  further emphasized the transparency of AI technologies in healthcare. These have presented new standards for evaluating the results from AI-related clinical trials [44▪▪,▪▪,In 2020, CONSORT-AI  and SPIRIT-AI  were announced to provide reporting guidance in AI-related clinical trials ▪▪. Subseals 44▪▪,▪▪,46 andHealthcare equity has been a major concern due to the imbalanced distribution of resources between urban and rural areas. The deployment of AI screening algorithms accompanied by well-established infrastructure of cybersecurity, including cloud-based systems or even home-based devices, can facilitate the adoption of AI technologies and reduce the medical resources gap between urban and rural areas. On the contrary, the quality of data-driven technologies may be affected by the inequality of data distribution , which reduces the generalizability of the AI models to specific populations due to the scarcity of related data. Overemphasizing an AI system without carefully considering the condition of health data poverty has potential to cause harm . Even ifRegulatory considerations for AI medical devices or software should include data security and sourcing, the design and development of algorithms, and evidence generation from AI-enabled technologies. Governmental regulatory bodies should provide clear guidance regarding the evidence requirements for AI medical devices and, to streamline the process of training, continuous education, and relicensing .For data privacy and confidentiality, in 2016 and 2017 the EU introduced the General Data Protection Regulation 2016/679 , the EU For adopting and marketing AI-based instruments and algorithms, different regulatory groups are responsible for ensuring the security and safety of the products. The U.S. Food and Drug Administration (FDA) announced the Digital Health Innovation Action Plan to streamline the premarket review and to outline its approach to AI-based frameworks, known as “software as medical device” , which iIn contrast to the United States, which adopts more market-oriented regulations, the EU takes a more customer-oriented approach to build the framework for AI-enabled technologies, with the Conformitè Européenne playing a critical role in the licensing of AI products . Many otFor adoption of AI technologies, achieving a practical balance between profit and cost is another important issue. Premarketing costs include significant effort and workforce in data collection, research development, and validation. Postmarketing costs include upgrading software, sustaining hardware over the long term, training operators, and incorporating new patient data ,56. FundThe cost of adopting AI-enabled technologies should be balanced between manufacturing price and reimbursements as it is considered by and for healthcare providers. In the example of Singapore's national DR screening programs, cost savings of approximately U.S. $21.9 million were achieved for a group of 170 000 patients with diabetes who underwent AI-assisted screening . In the Current AI-enabled systems with regulatory compliance are outlined in Table The Vision Academy recognizes the advantages of AI technology and recommends the use of them to be of additive and synergistic value to current standards of care. In terms of applying such technologies in diagnosing and screening retinal diseases, we summarize the following directions and emphasize several viewpoints important for the future.The integration of meta-data, including multimodal images and structured clinical information from multiple data sets with different ethnic groups, and establishment of a data processing and sharing system will empower data-driven AI technologies in ophthalmic practice. Ongoing research will be needed to build up data storage and sharing systems in a cybersecurity framework for broader use.While retinal images possess biometric information that could be reidentified by AI technologies, care should be taken when collecting and processing these images. Some novel learning tasks  can obscure bioidentical information or even provide unsupervised models for small-scale data sets. The question of how to universalize data formats will be one of the key factors for extending the scalability and generalizability of AI-enabled technologies.The complexity and inexplicability of AI are encompassed in the term “black box phenomenon.” Black box algorithms have potential to cause misuse of AI in healthcare ecosystems ,101▪. TrThe role of AI-enabled technologies in the real world is not to replace ophthalmologists but to assist them and to hybridize both AI models and human experience for making more efficient and accurate decisions. Such time-saving abilities could streamline medical procedures and give clinicians more time to communicate with their patients. Improper implementation of AI could be harmful to doctor–patient relationships and could affect patients’ trust if AI algorithms were used only for improving workflow but not patient care.A key hurdle in deploying AI-enabled technologies in clinical practice is the fear of making an incorrect decision and harming patients. Legal liability should be well defined as the implementation of AI becomes more popular. Such liability should only be at the precise claim of screening targeted diseases. Unlike retinal specialists, the developers of an AI model should only be liable for the designed algorithm for screening specific diseases. Healthcare providers should still take full responsibility for being aware of the capacity of AI models.The legal boundaries between developers and healthcare providers are still unresolved, and legislative and governance systems need to be more established to refine liability rules and the regulatory environment. Policy and specific authorities should be set up not only for verification of AI models but for data security and legal liability. Cross-sector and cross-disciplinary collaborations will be important to ensure the integrity of AI healthcare ecosystems.Continuing education, promotion of practical application, and user-friendly, understandable interfaces for healthcare providers are equally important to streamline the workflow and broaden the applicability of AI systems. Communication and collaboration between cross-functional teams, including ophthalmologists, optometrists, computer scientists, statisticians, data scientists, patient organizations, and engineers, can have a positive impact on vision health and preservation through AI-enabled technologies.The establishment of AI-enabled technologies may have potential to improve the efficiency of existing healthcare pathways, provide better patient-centered services, minimize the impact of labor shortage, and bridge the gap between urban and rural areas. However, no advancement in clinical practice is flawless, so it is necessary for healthcare providers and legislators to be aware of the limitations of AI-enabled devices.Editorial assistance was provided by Elle Lindsay, PhD, Macha Aldighieri, PhD, and Rachel Fairbanks, BA (Hons), of Complete HealthVizion, Ltd, an IPG Health Company, funded by Bayer Consumer Care AG, Pharmaceuticals Division, Basel, Switzerland.The Vision Academy is a group of over 100 international ophthalmology experts who provide guidance for best clinical practice through their collective expertise in areas of controversy or with insufficient conclusive evidence. The Vision Academy is funded and facilitated by Bayer. The opinions and guidance of the Vision Academy outputs are those of its members and do not necessarily reflect the opinions of Bayer.Financial arrangements of the authors with companies whose products may be related to the present report are listed in the “Conflicts of interest” section, as declared by the authors.Yu-Bai Chou is a consultant for Alcon and Bayer. Paolo Lanzetta is a consultant for Aerie, AbbVie, Apellis, Bausch & Lomb, Bayer, Biogen, Boehringer Ingelheim, Genentech, Novartis, Ocular Therapeutix, Outlook Therapeutics, and Roche. Tariq Aslam is a consultant for, and has received grants, speaker fees, and honoraria from, Allergan, Bayer Pharmaceuticals, Canon, NIHR, Roche, and Topcon. He is also a board member for the Vision Academy, Macular Society, and Fight for Sight charity. Jane Barratt has received honoraria from Bayer. Carla Danese is a consultant for Bayer. Bora Eldem is a consultant for Allergan, Bayer, Novartis, and Roche. Nicole Eter is an advisor for AbbVie, Alcon, Apellis, Bayer, Biogen, Janssen, Novartis, and Roche and has received speaker fees from AbbVie, Apellis, Bayer, Novartis, and Roche and research grants from Bayer and Novartis. Richard Gale is a consultant for AbbVie, Allergan, Apellis, Bayer, Biogen, Boehringer Ingelheim, Notal, Novartis, Roche, and Santen and has received research grants from Bayer, Novartis, and Roche. Jean-François Korobelnik is a consultant for Allergan/AbbVie, Apellis, Bayer, Carl Zeiss Meditec, Janssen, Nano Retina, Roche, and Théa and is a member of the Data and Safety Monitoring Boards for Alexion and Novo Nordisk. Igor Kozak is a consultant for Alcon, Bayer, and Novartis. Anat Loewenstein is a consultant for Allergan, Annexon, Bayer Healthcare, Beyeonics, Biogen, ForSight Labs, IQVIA, Iveric Bio, Johnson & Johnson, MJH Events, Nano Retina, Notal Vision, Novartis, Ocuphire Pharma, OcuTerra, OphtiMedRx, Roche, Ripple Therapeutics, Syneos, WebMD, and Xbrane. Paisan Ruamviboonsuk is a consultant for, and has received research funds from, Bayer and Roche. Taiji Sakamoto is a consultant for Bayer Yakuhin, Boehringer Ingelheim, Chugai, Nidek, Nikon, Novartis, Santen, and Senju. Daniel S.W. Ting has received research grants from the National Medical Research Council Singapore, Duke-NUS Medical School Singapore, and Agency for Science, Technology and Research Singapore. Peter van Wijngaarden is the co-founder of Enlighten Imaging, an early-stage medical technology start-up company devoted to hyperspectral retinal imaging and image analysis, including the development of AI systems, and has received research grant support from Bayer and Roche and honoraria from Bayer, Mylan, Novartis, and Roche. Sebastian M. Waldstein is a consultant for Apellis, Bayer, Boehringer Ingelheim, Novartis, Roche, and Santen. David Wong is a consultant for AbbVie, Alcon, Apellis, Bayer, Bausch Health, Biogen, Boehringer Ingelheim, Novartis, Ripple Therapeutics, Roche, Topcon, and Zeiss, has received financial support (to institution) from Bayer, Novartis, and Roche, and is an equity owner at ArcticDx. Lihteh Wu is a consultant for Bayer, Lumibird Medical, Novartis, and Roche. Miguel A. Zapata is a consultant for Novartis and Roche, has received grants and speaker fees from DORC, Novartis, and Roche, honoraria from Alcon, Bayer, DORC, Novartis, and Roche, has served on advisory boards for Novartis and Roche, has received equipment from Allergan, and has stock or stock options in UpRetina. Javier Zarranz-Ventura has received grants from AbbVie, Allergan, Bayer, Novartis, and Roche, has served on scientific advisory boards for AbbVie, Allergan, Bayer, Novartis, and Roche, and has been a speaker for AbbVie, Alcon, Alimera Sciences, Allergan, Bausch & Lomb, Bayer, Brill Pharma, DORC, Esteve, Novartis, Roche, Topcon Healthcare, and Zeiss. Aditya U. Kale, Xiaorong Li, and Xiaoxin Li have no conflicts of interest to report.


Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI’s precision and analysis regarding patients’ genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI’s contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI’s capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI’s impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential. In the modern world, the doctor-patient encounter has rapidly risen due to the introduction of artificial intelligence (AI). The cost of healthcare and the uneasy accessibility form the primary issues when it comes to the availability of healthcare in rural and urban areas. Besides, the number of treatment providers and the inability to cope with rising technology is also becoming a major problem. Therefore, to cope with the rising spread of disease and to be able to provide a more budget-friendly and timely cure, AI is the next best step .The tiresome task of drug development and medicine manufacture, which required heavy investments and human intervention earlier, has now been reduced to a click. Ranging from cure identification to execution of its use has all become an AI-monitored task. Hence, effectiveness and efficiency are considerably targeted when the role of machines and new technology comes into play .With accumulated laboratory technological data combined with analysis of patient history, AI proved to be nothing short of a blessing during the COVID-19 pandemic. A complete review of the post-COVID-19 syndromes helped to improve vaccine development. Thus, AI is a multifaceted approach to not just curative but also preventive measures .While there is no doubt that AI has revolutionized healthcare to its core and is continuing to do so, ethical and private concerns form a major part of the demerits associated with it. The first problem is the unavailability of credible data. The second problem is algorithm dysfunction. Safe storage of data and elimination of breach of information forms the basis of patient privacy. Ethically, genomic replication or even human clone formation is resented by many cultures and religions worldwide . TherefoSelection processWe defined the criteria for selecting articles, including publication date, relevance to the topic, study design, and geographic scope, and used multiple databases  and keywords to search for articles related to the topic and to ensure we capture all relevant studies. Subsequently, the authors reviewed titles, abstracts, and full texts to identify potential articles meeting inclusion criteria and excluded those that did not meet the criteria. We also checked reference lists of selected articles to find additional relevant sources that may have been missed initially. Some authors evaluated the quality of selected articles based on factors such as study design, methodology, sample size, and author credibility. Finally, we extracted the key information from each selected article, including findings, methodologies, and conclusions, and organized this information systematically.Artificial intelligence-driven diagnostics and imagingAI employs algorithms such as machine learning (ML) and deep learning (DL) to diagnose diseases in a time-efficient manner. This helps diagnose diseases early and reduces complications . A cliniTime is crucial for the early identification and containment of epidemics. AI-based deep Q learning auxiliary diagnosis (DQAD) was used to diagnose epidemics, reducing the time of diagnosis compared to the current methods . Using cA highly efficient shock advice algorithm (SAA) is essential for an automated external defibrillator to diagnose and function correctly. A new SAA with DL and ML algorithms had better sensitivities and specificities than the existing SAA. Researchers used magnetic resonance imaging (MRI) histogram peaks to design AI algorithms that accurately detect tumor volumes, with improved specificity, sensitivity, and interoperator repeatability. ML models combining radiomics and clinical indicators have successfully predicted molecular subgroups of medulloblastomas and differentiated skull base chondromas from chondrosarcomas .Diagnosing stroke early is essential as a delay in endovascular thrombectomy (EVT) reduces effectiveness. MethinksLVO (large vessel obstruction), an ML software, uses non-contrast computed tomography (NCCT) to identify the LVO location to facilitate the transfer of patients to EVT facilities early, thus improving outcomes . The useThe role of AI in diagnosing certain types of cancers, such as pancreatic cancer, which is often diagnosed in late stages, could be life-changing for many patients. Many challenges occur in the diagnosis, such as late symptom onset, inconsistent symptoms, and lack of molecular markers. AI tools such as K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) are studied to identify subtle abnormal imaging findings that aid in the early detection of pancreatic cancer . The AI-Biopsy remains the gold standard method for diagnosing skin cancers. Using DL algorithms on fluorescence spectroscopy, basal cell carcinoma (BCC) was diagnosed in real time. DSL-1 was the device employed, which used the fluorescence spectra data from BCC lesions and normal skin as a base to identify the lesions . A studyTumor borders that are difficult to identify become challenging during surgery, such as gliomas, depending on frozen sections for delineating the boundaries in real time but at the cost of time. Fluorescent light CNN (FLCNN) was studied using the near-infrared window (NIR-II) images as an alternative or in conjunction with the surgeon’s optical judgment. The study showed that the FLCNN captured the details of the image better than the surgeon .AI’s role in diagnosing neurological diseases is being explored further . BattineCurrently, the diagnosis of obstructive sleep apnea (OSA) is made by polysomnography (PSG) which employs the apnea-hypopnea index (AHI). AI-based CNN algorithm uses a single-lead ECG signal to diagnose OSA with reasonable accuracy, sensitivity, and specificity. This method is easy and efficient but still needs PSG to confirm if the estimated risk is high . AI’s roThe use of AI in diagnosing hematologic disease is on the rise. Cellavision, a peripheral blood smear analyzer approved by the Food and Drug Administration (FDA), utilizes the ML-ANN algorithm to perform tests such as complete blood count, cell differential, identification, and location . MorphogAI-driven diagnostics and imaging are proliferating and finding applications in every possible field of medicine. Further exploration into these algorithms can be assimilated to provide better patient care, considering the ethics and social regulations .Personalized treatment and precision medicineArtificial Intelligence in Genomics and Its Role in Personalized Treatment PlansPersonalized medicine is a method that considers an individual’s unique genetic makeup, environment, and lifestyle for treating and preventing diseases . It shifML is a method that employs mathematical algorithms to construct models using datasets; these models can enhance their performance through experience . Among tFunctional genomics explores the intricate interplay between genetic attributes and environmental conditions. This endeavor has been significantly aided by the utilization of advanced deep architectures . For insin silico nano dissection, an ML algorithm, to study mRNA expression in glomeruli from IgA nephropathy patients [Significant strides have been made in applying functional genomics to enhance precision medicine for prevalent non-cancerous conditions, such as kidney disease . An exampatients . They idpatients . In anotpatients .ML models have the potential to analyze multimodal data obtained from electronic medical records and other curated sources to identify patients who could benefit from early treatment or participation in randomized control trials of innovative interventions . For insDrug Discovery and Development With Artificial Intelligence AssistanceAI is already deeply embedded in almost every facet of modern society. In exchange, it is undergoing rapid innovation. Because of this, it is rapidly gaining importance in the pharmaceutical industry, particularly in the realm of drug research and development .Drug development is time-consuming and labor-intensive, predicated on iterative trial-and-error experimentation and high-throughput screening processes. On the other hand, using algorithms such as ML and natural language processing (NLP) will significantly speed up the entire process while also requiring substantial data inputs. Errors may be reduced, and the quality of analytical findings improved by streamlining the process .Using the DL method makes it easier to ascertain the effectiveness of a substance. Using AI techniques, we can also determine if a medicine is toxic . How AI AI makes use of a number of methods, including reasoning, knowledge representation, problem resolution, and a model of ML. This analysis will focus on the SVM classification model. SVM is a supervised machine learning module that generates analytical outputs by classifying two distinct sets of troublesome subgroups using a hyperplane between their extreme edges. Therefore, with the use of a decision boundary or hyperplane, the two sets can be organized into usable data .perceptons that are analogous to biological neurons. An ANN is a collection of nodes that accept various inputs and convert them to the output, either individually or in a multi-link configuration, using algorithms to solve problems [ANNs are also used in the domain of ML known as DL. These can be described as a collection of high-tech computers that mimic the way in which the human brain sends and receives electrical signals by using problems .Anticipating global infectious disease hazards using AI-based surveillance models may be very helpful ,54. The Artificial intelligence-enabled patient monitoring and disease managementPublic health surveillance faces data sourcing and analytics challenges in identifying reliable signals of health anomalies and disease outbreaks from data sources. AI helps tackle these challenges by enabling and enhancing public health surveillance through methods and techniques such as DL, reinforcement learning, knowledge graphs, Bayesian networks, and multiagent systems. AI enhances surveillance of various open novel or unexplored data, unstructured and semi-structured data, complex spatiotemporal data, and epidemics’ evolution, which is difficult with traditional surveillance methods .Remote patient monitoring (RPM) or telemonitoring uses digitally transmitted health-related data to improve patient care through education, early disease decompensation detection, intervention, and enhanced patient-physician relationship . TelemonAn ML approach predicted cardiac arrest within 24 hours more accurately than the traditional modified early warning scores for critically ill patients in the emergency department . A retroVipin et al. proposed an edge AI-enabled Internet of Things (IoT) healthcare monitoring system for the real-time scheduling of patients and provision of resources on a priority basis depending on the patient’s condition. This tool collects and transmits data and triggers appropriate action on other integrated devices, simplifying the process of monitoring and assistance provision, which is particularly useful in the elderly or disabled, and in pandemic situations .The FDA issued Emergency Use Authorization for wearable and mobile ECG technologies to record QT intervals in patients taking hydroxychloroquine or azithromycin. However, AI-ECG technologies need further evaluation for external validity in diverse populations to be applied in routine clinical practice . AI-baseArtificial intelligence for remote and telemedicineTelemedicine refers to delivering healthcare services using information and communications technologies, considering distance and accessibility as critical factors. Initially envisioned for tech-savvy patients, the COVID-19 pandemic propelled telemedicine into mainstream healthcare globally . Its scaA typical deep neural network utilized for processing and analyzing visual images is the CNN . This neSimilarly, age-related macular degeneration (AMD) is a significant cause of vision loss among the elderly population globally. However, the growing number of AMD patients and frequent follow-ups create a pressing need for a robust automatic mechanism. DL and telemedicine offer potential solutions for AMD management, including initial screening, subsequent monitoring, and treatment prediction. DL algorithms can assess visual acuity (VA) and optical coherence tomography (OCT) findings, aiding in determining AMD treatment strategies .In the mental health domain, a smartphone-sensing wearable device was proposed to assess behavior and identify depressive and manic states in patients with bipolar disorder . PatientTelemedicine, especially in dermatology, has rapidly embraced AI due to increasing demand, the necessity for high-quality images, and the availability of advanced technology . While iIn neurosurgery, telemedicine demonstrates its potential with applications such as telehealth stroke triage and ML-based prognostics for neuro-trauma . RecentlIn diabetes management, several projects have emerged utilizing new information and communication technologies and Web 2.0 technologies for automatic data transmission and remote interpretation of patient information -93. A noArtificial intelligence-driven behavioral health interventionsExpanding access to digital mental health therapies, the increasing need for psychological services, along with the development of AI, has led to the rise of digital mental health interventions (DMHsI) in recent years. Patient evaluation, symptom evaluation, modifying behaviors, and information dissemination are just a few of the areas where AI-powered chatbots are currently being employed in DMHIs. Chatbots may be as basic as algorithms based on rules such as ELIZA or as complex as AI models that use ML and language processing methods .Support, screening, training, intervention, surveillance, and avoiding recurrence are just a few of the chatbot-based services offered inside DMHIs. Using chatbots for mental health diagnosis and triage might reduce the burden on healthcare workers and expedite the treatment of individuals who need it the most . ChatbotOverall, 51% of mental health professionals surveyed thought using chatbots for diagnostic purposes was a challenge. AI-assisted diagnostics, on the other hand, might aid in the early identification of those at risk, allowing for more effective intervention and problem avoidance ,107. To tracks to assist users in zeroing in on a specific issue, such as stress management [For instance, certain chatbots leveraging NLP in nature may mimic a therapeutic conversational approach for the purpose of deploying and educating users about different therapies . The mosnagement .Anna may be found in these tracks. After selecting a route that includes Anna, users are welcomed by the chatbot with a personalized video message and explanation of the chatbot’s capabilities. Listeners may also be polled by Anna with specific questions aimed at collecting information for refining the personalized playlist. To increase user involvement and the likelihood that they will complete the activities as intended, Anna also provides particular assignments inside these tracks. So, Anna helps make the most of the benefits of that move. Anna tracks the elements that contribute most to successful outcomes for each activity, evaluates responses based on those elements, and then prompts participants to fill in any gaps in their initial remarks. Whenever a user’s first response to a task requiring gratitude does not demonstrate an adequate amount of gratitude, Anna will ask for clarification. Anna guides users on how to make better use of the platform and listens to their feedback to allay concerns that conversations with chatbots are not engaging enough .Artificial intelligence in medical education and trainingWith the increased integration of AI in healthcare, electronic health records (EHRs) can be used for novel techniques such as data processing and enhanced decision-making, prompting accurate data input into EHRs by physicians to maximize the benefits of AI . These sA scoping review by Nagi et al. on current applications of different AI methods such as ML, robotics training, and virtual reality (VR) in several domains of medical education has shown enhanced practical skills of medical students following the implementation of AI-based assistance. Through VR, students can train their decision-making skills in surgical and medical procedures in a controlled and safe manner . DespiteCreutzfeldt et al. trained 12 Swedish medical students in cardiopulmonary resuscitation by avatars to better understand their reactions and experiences using a multiplayer virtual world. The positive aspects of learning cardiopulmonary resuscitation were confirmed after a data-driven approach in qualitative methodology. Further clinical performance should be analyzed to rule out erroneous self-belief bias. Participants noted insufficient psychomotor skills and lack of stress to be unrealistic, which could affect their real-world performance .A meta-analysis by Zhang et al. showed that AI could also be applied in medical education at different stages, such as teaching implementation, evaluation, and feedback. The quality of teaching can be assessed with feedback and evaluation from AI. However, it is challenging to verify the effectiveness of AI implementation . TeacherAlthough ChatGPT has revolutionizing potential in medical education, it cannot completely replace hands-on clinical experience and mentorship. Moreover, algorithms use large databases, which may contain biases resulting in biased system output that could lead to a loss of fairness in treating minority or underrepresented groups. Therefore, AI systems output should be monitored to ensure the absence of bias and eliminate any biases present . FurtherThe use of AI is not without its challenges, such as black box problems , data prArtificial intelligence ethical considerationsHealthcare is a high-risk field in which mistakes can lead to severe consequences for the patient. Patients meet with physicians at a time when their overall health is already compromised, leaving them vulnerable . Therefoblack box issue, where the algorithms responsible for the outputs are not disclosed [The main legal concern is the limited transparency of algorithms used in AI, referred to as the isclosed ,134. Traisclosed . SuccessThe FDA has already approved autonomous AI diagnostic systems based on ML. These ML-healthcare applications (ML-HCAs) create algorithms from large datasets and make predictions without requiring explicit programming to avoid biases and errors . While tPrivacy protection poses challenges to ML. ML necessitates large datasets for effective training, leading to high accuracy in results. However, when significant amounts of data are used to train ML applications from multiple sources, the data’s origin can ultimately be traced back to the patients, negating attempts at privacy . Ownershphysical evidence related to patient data [In Europe, patients own and have usage rights to all their data, whereas healthcare providers in North America may own the ent data ,139. Witent data  in the Eent data .Accountability for AI errors remains a gray area, with no clear individual or entity held responsible. Consequently, AI severely limits the ability to assign blame and/or ownership of the decision-making process . The lacIn the study conducted by Buolamwini and Gebru, selection bias was evident in automated facial recognition and the associated datasets, resulting in reduced accuracy in recognizing darker-skinned individuals, particularly women. The datasets used by MLs are derived from specific populations. Consequently, when these AI systems are applied to underserved or underrepresented patient groups, they are more likely to produce inaccurate results . Due to The contentious debate surrounding the legality of AI systems has prompted governing bodies such as the European Parliament to pass the GDPR , aimed aChallenges and future directionsAI is a rapidly evolving technical discipline that plays a crucial role in medicine. However, the implementation of AI faces several challenges, including technical, ethical, safety, and financial concerns. AI technology offers significant opportunities for diagnostic and treatment purposes in medicine, but it requires overcoming challenges, particularly related to safety . One of The integration of AI into cancer research shows promising outcomes and is currently addressing challenges where medical experts struggle to control and cure cancer. AI provides tools and platforms that enhance our understanding and approach to tackling this life-threatening disease. AI-based systems aid pathologists in diagnosing cancer more accurately and consistently, thereby reducing error rates . AI has Despite the considerable advantages and notable progress in contemporary methodologies, rule-based systems commonly employed in medical AI exhibit limitations. These encompass substantial expenses associated with development, constraints arising from the intricate representation of multifaceted connections, and the need for extensive medical expertise .While AI’s involvement in direct patient care is currently limited, its expanding role in complex clinical decision-making processes is on the horizon. Establishing fundamental guidelines for AI’s scope, transparent communication with patients for informed consent, and comprehensive evaluation of AI’s implementation are crucial steps toward setting a universal standard. This emphasizes the importance of algorithmic transparency, privacy protection, stakeholder interests, and cybersecurity to mitigate potential vulnerabilities. Proactive leadership from professional organizations can play a vital role in building public trust in the safety and efficacy of medical AI, thereby driving further advancements in this promising domain .AI has the potential to collaborate with other digital innovations such as telemedicine, enabling virtual consultations and the implementation of the Internet of Medical Things to enhance referral procedures. AI’s capabilities extend to precise risk stratification of cancer stages and the selection of suitable treatment paths. Shared factors are evident across multiple tiers during data analysis, enabling AI models to identify causal links between variables. By amalgamating this knowledge, AI research can achieve higher precision in examining cancer-related medical incidents. In the future, comprehensive databases will emerge, generating new data sets encompassing every aspect of human health. These repositories will empower highly intricate models, capable of personalizing therapy choices, precise dosage calculations, surveillance strategies, timetables, and other pertinent factors .Prominent AI technologies such as ML and DL have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has changed the face of healthcare through unique advancements in patient monitoring, diagnosis, and treatment planning. AI has made strides in optimally implementing clinical drug trials, telemedicine, and personalized therapeutic regimes. However, ethical issues regarding data protection and transparency of AI-driven algorithms remain a cause for concern. Although AI holds promise in mental health interventions, medical education, and virtual training, its role must be aligned with human expertise. The main challenge is to optimize AI’s transformative impact while keeping ethical and regulatory principles in line.


Artificial intelligence (AI) has been available in rudimentary forms for many decades. Early AI programs were successful in niche areas such as chess or handwriting recognition. However, AI methods had little practical impact on the practice of medicine until recently. Beginning around 2012, AI has emerged as an increasingly important tool in healthcare, and AI-based devices are now approved for clinical use. These devices are capable of processing image data, making diagnoses, and predicting biomarkers for solid tumors, among other applications. Despite this progress, the development of AI in medicine is still in its early stages, and there have been exponential technical advancements since 2022, with some AI programs now demonstrating human-level understanding of image and text data. In the past, technical advances have led to new medical applications with a delay of a few years. Therefore, now we might be at the beginning of a new era in which AI will become even more important in clinical practice. It is essential that this transformation is humane and evidence based, and physicians must take a leading role in ensuring this, particularly in hematology and oncology. Currently, the majority of AI applications in cancer medicine are related to digital image processing in fields such as dermatology, endoscopy, radiology and pathology . Breakthroughs in large language models (LLMs) have moved this field of AI in the center of public attention. LLMs are AI models which can understand and synthesize text with human-level performance. LLMs can understand, summarize and write scientific articles, understand and write computer code and medical texts, they can converse and make jokes. Google, Microsoft-backed OpenAI and other large technology companies are pushing LLMs towards medical applications, such as information retrieval, summary, chatbot functionalities, among many other potential applications (Singhal et al. The rapid development of AI requires physicians to stay up to date and comprehend the medical implications of these technologies. Being digitally literate and capable of critically evaluating clinical evidence in the AI era is a fundamental skill which physicians must build and cultivate. All physicians must become aware of AI and comprehend its fundamental principles. In addition, some physicians might choose to delve deeper into AI and actively use it as a tool for research. By enabling the processing of large amounts of unstructured information, AI could fundamentally alter and transform nearly all aspects of contemporary medicine, including preclinical research, drug discovery, clinical trials, and even clinical routine activities, including communication. We are living in exciting times and the world of hematology and oncology as we know it will change in the future Topol . In thes


Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. Specifically, we discuss the ethical principles affected by AI approaches to human neuroscience and provisions that might be imposed in this domain to ensure that the benefits of AI frameworks remain in alignment with ethics in research and healthcare in the future. Artificial intelligence (AI) leverages software to digitally simulate the problem-solving and decision-making competencies of human intelligence, minimize subjective interference, and potentially outperform human vision in determining the solution to specific problems.Over the past few decades, neuroscience and AI have become to some degree, sublimated with the development of machine learning (ML) models using the brain circuits as the model for the invention of intelligent artifacts .Neuroscience inspired and then ironically, validated the architecture of various AI algorithms  such as Traditional, clinical neuroradiologists identify abnormalities of the spine, head and neck, and spinal cord through pattern abnormalities on MRI and CT . Radiomics and AI leverage imperceptible variations in images. Some have termed this as seeing the unseen. Research on applying AI to neuroradiology, and all imaging, has rapidly grown over the past decade. The number of scholarly publications related to the development and application of AI to the brain has more recently increased astoundingly .The immense increase in publications on the development of AI models shows that AI is rapidly gaining importance in neuroradiologic research and clinical care. The growth in research is related to the combination of more powerful computing resources as well as more advanced measurement techniques –8, combiFor neuroradiology, a deep learning (DL) model receives image series in the input layer, then the extracted features are analyzed in the hidden layer using various mathematical functions. The output layer encodes the desired outcomes or labeled states . The goal of training a DL model is to optimize the network weights so that when a new series of sample images are fed to the trained model as inputs, the probabilities measured at the output are heavily skewed to the correct class  Figure .For the short term, these automatic frameworks will likely serve as decision-support tools that will augment the accuracy and efficiency of neuroradiologists. However, the progress in developing these models has not corresponded with progress in implementation in the clinic . This maIn this paper, we contribute to the ethical framework of AI in neuroradiology. Below we present several specific ethical risks of AI, as well as air some principles that might guide its development in a more sustainable and transparent way. In our review, we highlight the ethical challenges that are raised by providing input for the AI models and the output of the established frameworks in neuroradiology . In eachThe value of any recorded medical observation - whether it is images, physical examination results, or laboratory findings - primarily lies in how they contribute to that patient's care. However, when the data are collected (and anonymized), this data can also be used to generate useful information for potential research. This information also may eventually have commercial potential. Databases can help us understand disease processes or may generate new diagnostic or treatment algorithms. These databases can be used to assess therapies in silico.Because of the value of medical data, especially images, when conglomerated, those who participate in the health care system have an interest in advocating for their use for the most beneficial purposes. Hence, the controversy regarding who has the right to control and benefit from collected images, patients, or provider organizations.There has been a paradigm shift over the last decade with respect to the ethos of data ownership . While sMost AI scientists believe in the ethical responsibility of all patients to share their data to improve patient care, now, and in the future. Thus, these researchers believe the data should be widely available and treated as a form of public good to be used for the benefit of future patients , 22. IndThe significance of the diligent and ethical use of human data has been highlighted recently to promote a culture of data sharing for the benefit of the greater population, while also protecting the privacy of individuals . TherefoOther issues specifically related to neuroradiology, such as the presence of personal identifiers, which need to be removed both from the digital imaging and communications in medicine (DICOM), metadata, as well as from the images prior to data sharing . For exaRegarding the increasing usage of digital data in neuroscience, it is important to manage data resources credibly and reliably from the beginning. Data governance is the set of fundamental and policies that illuminate how an organization manages the data assets . Data goCentralized and distributed networks are two organized methods that have been developed for tactical governance of data sharing . While iThe quality and amount of the annotated images for training AI models are variable, based on the target task. Although using poor-quality images may lead to poor predictions and assessments, in heterogeneous large quantities, it is known that AI algorithms can be trained on relatively poor-quality images , 33. HowMost currently implemented AI algorithms for medical image classification tasks are based on a supervised learning approach. This means that before an AI algorithm can be trained and tested, the ground truth needs to be defined and attached to the image. The term ground truth typically refers to information acquired from direct observation . For direct observation references standards images are annotated by medical experts- such as neuroradiologists.Manual labeling is often used in the labeling and annotation of brain imaging data for AI applications. When a relatively small number of images are needed for AI development, medical expert labeling and segmentation may be feasible. However, this approach is time-consuming and costly for large populations, particularly for advanced modalities with numerous images per patient, such as CT, PET, or MRI. Prior to the widespread use of AI, and markedly improved through AI, semiautomated and automated algorithms have become somewhat widely used in labs.Radiology reports are not created for the development of AI algorithms and the extracted information may contain noise. Although, recently more reports are structured, or protocol-based, more often they are narrative. These narrative reports have previously been assessed through natural language processing programs.Neural networks can still be relatively robust when trained with noisy labels . HoweverAI could serve a role to reduce these errors and may help in the more accurate and time-efficient annotation of CT and MRI scans. However, this assumes a sufficiently sized dataset to adequately train the AI models. Furthermore, recent efforts in the automation of the annotation process particularly in neuroimaging data have shown a significant increase in the performance of the annotation process by using AI systems in large scale of data. For example, a study in brain MRI tumor detection indicated that applying semi-supervised learning to mined image annotations can improve tumor detection performance significantly by achieving an F1 score of 0.95 .There is a trend toward interactive collaboration between AI systems and clinical neuroradiologists. The ability to self-validate and learn from mistakes of AI systems makes the system able to recognize their errors and self-correct their own data sets. On the other hand, external validation by neuroradiologists, manually fine-tuning the AI system, that it has made an error, and allowing the AI system to update its algorithm to avoid errors is helpful for the improvement of these models. Such interactive collaboration annotations have been used effectively for labeling open-source data sets to improve annotation quality while saving radiologists time , 39.Neuroradiology is overall the third most common imaging subspecialty , and thiA recent review paper explored the application of AI in neuroradiology and identified the majority of these applications provide a supportive role to neuroradiologists rather than replacing their skills. For example, assessing quantitative information such as the volume of hemorrhagic stroke and automatically identifying and highlighting areas of interest such as large vessel occlusions on CT angiogram . AI can While these capabilities are very promising, AI systems are not without limitations. For an AI system's algorithm to accurately work, they require large datasets and accurate expert labeling that may not always be possible. Therefore, as Langlotz suggested , maybe tDespite the widespread application of AI, it is important that radiologists remain engaged with AI scientists to both understand the capabilities of existing methods and direct future research in an intelligent way that is supported by the sufficient clinical need to drive widespread adoption . An AI-nThe subject of explicability is crucial as AI models are still considered as black box, due to lack of clarity regarding the data transformations after various passages within the convolutional neural networks (CNNs) despite the mathematical and logical processes . TherefoThere is insufficient practicality related to the final stages such as reporting and communication compared to the early stages of the AI application  . This inThe emergence of using AI in neuroradiology research has highlighted the necessity of code sharing because these are key components to facilitating transparent and reproducible experiments in AI research . HoweverAlthough these low rates of code sharing and code documentation may be discouraging, it has shown that code sharing over time is an uphill journey . NonetheIn addition to code sharing, data availability is another key component of the reproducibility of AI research studies, because DL models may have variable performance on different neuroimaging datasets . HoweverMedical imaging, including MRI, is one of the most common ways of brain tumor detection. MRI scans such as T2-weighted and post-contrast T1-weighted are preferred since they provide more precise images and provide better visualization of soft tissue, therefore they can be used for brain tumor segmentation . CNNs, aBias is an important ethical theme in research and clinical care. It is quite easy for potential bias to be embedded within algorithms that grow from “selected” data that are used to train algorithms , 54. MosA recently published review by Das et al.  investigSome studies suggested using AI itself to potentially mitigate existing bias by reducing human error and biases that are present within healthcare research and databases . These sIn this section, we explore a frequent question regarding the application of AI in neuroradiology as to who is responsible for errors that may happen through the process of developing and deploying AI technology. AI algorithms may be susceptible to differences in imaging protocols and variations in patient numbers and characteristics. Thus, there are by the nature of the beast, specific scenarios where these algorithms are less reliable. Therefore, transparent communication about selection criteria and code sharing is required to validate the developed model by external datasets to ensure the generalizability of algorithms in different centers or settings .Despite the success and progress of AI methods, they are ultimately implemented by humans, hence, some consideration of user confidence and trust  is imporSeveral studies recommend that since healthcare professionals are legally and professionally responsible for making decisions in the patient's health interests, they should be considered responsible for the errors of AI in the healthcare setting, particularly with regard to errors in diagnostic and treatment decisions , 66. HowThe use of AI technology in neuroradiology should be cognizant that ultimately it is a person or persons who are responsible for the proper implementation of AI. Part of this responsibility lies in the appropriate implementation and use of guidelines to minimize both medical errors and liability. Guidelines should be implemented to reduce risks and provide reasonable assurance including the well-documented developed methods, research protocols, appropriate large datasets, performance testing, annotation and labeling, user training, and limitations . EspeciaWhen we do research, we have a moral obligation to provide the highest quality product we can. That is why IRBs not only look at the risks and benefits but also the research protocol, to ensure that the results will be worthwhile. One of the aspects of this IRB assessment is the size and type of population studied. Similarly in AI research, the database should include a large enough number of subjects to avoid overfitting. In addition, an external validation set is ethically required, to ensure generalizability. The numbers needed in the development set have been a moving target but are appropriately moving towards substantive requirements.Dataset size is a major driver of bias and is particularly associated with the size of training data; the AI models should be trained on large, heterogenous, annotated data sets . Small tPrevious studies also have shown that using a large dataset decreases model bias and yields optimal performance because each MRI technique has different characteristics, therefore, integrating various modalities and techniques yields more accurate results than any single modality. Using multimodality and heterogenous datasets also can handle the overfitting problem regarding the trained model using a specific dataset to make the model generalize for an external validation set , 73.In diseases, or neurologic disorders that are less common, finding large enough datasets may be difficult and may require pooled resources. When providing larger datasets is not possible due to rare diseases and under-represented populations, transfer learning, and data augmentation can be used to avoid overfitting due to small or limited data sets . The stuSeveral ethical themes of the applications of AI in neuroradiology were presented in this review including privacy and quality of data that are used for training AI systems as well as the availability and liability of the developed AI models. In this scoping review, we addressed the ethics of AI within neuroradiology and reviewed overarching ethical concerns about privacy, profit, liability, and bias, each of which is interdependent and mutually reinforcing. Liability, for instance, is a noted concern when considering who is responsible for protecting patient privacy within data-sharing partnerships and for AI errors in patient diagnoses. We note that liability is related to specific laws and legislation, which by definition vary from one to another jurisdiction.These broad ethical themes of privacy and security, liability, and bias have also been reported in other reviews on the application of AI in healthcare and radiology in general, and neuroradiology in particular. For example, in a review by Murphy et al. , the autIn another study, the authors discussed ethical principles including accountability, validity, the risk of neuro-discrimination, and neuro-privacy that are affected by AI approaches to human neuroscience . These lAnother article with a focus on patient data and ownership covered key ethical challenges with recommendations towards a sustainable AI framework that can ensure the application of AI for radiology is molded into a benevolent rather than malevolent technology . The othIn conclusion, the ethical challenges surrounding the application of AI in neuroradiology are complex, and the value of AI in neuroradiology increases by interdisciplinary consideration of the societal and scientific ethics in which AI is being developed to promote more reliable outcomes and allow everyone equal access to the benefits of these promising technology. Issues of privacy, profit, bias, and liability have dominated the ethical discourse to date with regard to AI and health, and there will undoubtedly be more that arise. AI is being developed and implemented worldwide, and thus, a greater concentration of ethical research into AI is required for all applications amidst the tremendous potential that AI carries, it is important to ensure its development and implementation are ethical for everyone.


AI-based prediction models demonstrate equal or surpassing performance compared to experienced physicians in various research settings. However, only a few have made it into clinical practice. Further, there is no standardized protocol for integrating AI-based physician support systems into the daily clinical routine to improve healthcare delivery. Generally, AI/physician collaboration strategies have not been extensively investigated. A recent study compared four potential strategies for AI model deployment and physician collaboration to investigate the performance of an AI model trained to identify signs of acute respiratory distress syndrome (ARDS) on chest X-ray images. Here we discuss strategies and challenges with AI/physician collaboration when AI-based decision support systems are implemented in the clinical routine. Indeed, AI seems to be everywhere, from the realistic appearing conversations of ChatGPT to the image recognition software allowing many of us to unlock our cellphones with a glance. Within healthcare, both excitement and concern about AI are nothing new.In a recent New York Times editorial, an author dubbed artificial intelligence (AI) a “Pandora’s box” that humans were lifting the lid of2, ophthalmology3, and radiology4. Thus far, the prevailing thought within healthcare has been that AI will be most useful within clinical decision support systems (CDSS). CDSS aim to aid clinicians with the complex decision-making process to improve healthcare quality. Where exactly AI fits best within the decision-making process remains debated5.Over the last years, several AI-based models have demonstrated their usefulness in various medical disciplines, including dermatology5 systematically determined how AI could be integrated into a specific clinical scenario. They took an AI model6 trained to identify patterns of acute respiratory distress syndrome (ARDS) on chest X-ray images and evaluated its strengths and weakness compared to physicians. In doing so, they tested four different physician–AI collaboration strategies. One strategy involved the AI model reviewing a chest X-ray first and then deferring to a physician in cases of uncertainty. This strategy achieved higher diagnostic accuracy compared to the other three, which included physicians reviewing the X-ray first and deferring to the AI model in cases of uncertainty, the AI model examining the X-ray alone, or the physician examining the X-rays alone.In a recent case study, Farzaneh et al.5. This could mean that those caring for ARDS patients can use AI models to help triage these patients when the X-ray findings are clear, while physicians focus on interpreting the more complicated images.Ultimately, these findings imply that the AI model had higher and more consistent accuracy on less complicated chest X-rays, while physicians had higher accuracy on difficult chest X-raysDespite the promises of AI-based CDSS, obstacles are blocking its implementation. Four key challenges to the widespread use of AI-based CDSS include trust, bias, scalability, and deployment.8. This study by Farzaneh et al. 5 is an example of the type of research that must continue to be done to evaluate and build trust in AI/physician collaboration workflows. Additionally, AI models will need to be explainable, describing why and which parameters impacted a model’s decision. For example, visualizing alerts or displaying regions of concern in an X-ray can help to overcome the distrust in the “black-box” nature of most AI-based systems9. On the other hand, excessive trust and reliance on a CDSS can interfere with developing clinical skills10.Both patients and clinicians must trust AI models used for decision support if they are to be widely adopted12. For example, AI models trained on a dataset with unequal representation of a particular minority group might generate less accurate predictions for that minority patient population, leading to worse patient care. Various strategies for detecting and mitigating bias13 have been developed to tackle this issue, but further approaches are pivotal for generalizability and fairness.Datasets used to train AI models can contain bias, amplifying existing inequity in the healthcare system. This type of bias specifically harms disadvantaged populations5 describe a specific case study that helps optimize AI-physician collaboration, it is unrealistic to expect every implementation of AI support to occur only after a published trial. Instead, AI-CDSS will likely scale based on inferences from studies of similar clinical challenges. The specific point at which AI will be implemented in a workflow will ultimately differ among healthcare settings.While Farzaneh et al.14. Other challenges include insufficient IT infrastructure in under-resourced clinical settings where building on AI is challenging. As technical complexity increases, increased computer literacy and proficiency will be required. Lacking these skills can be hindering for clinical decision support systems adoption15.Looking into the future, generalizable CDSS tools will be implemented in healthcare settings in which they were not actually developed. The details surrounding how such generalizable tools will be developed and implemented in local workflows remain up to question16.Challenges to AI-based CDSS on the deployment level are centered around regulatory concerns and long-term effects. AI-based CDSS will require new rules around where responsibility lies for potential mistakes. To take just one example, not using physician decision support systems could be considered malpractice by an individual physician, or a healthcare institution that has adopted the AI-CDSS tool17. For example, for medical students AI tools could provide a three-dimensional virtual reality experience that can change the way of Anatomy teaching and learning18. Further, as medical training during the COVID-19 pandemic became challenging, surgeon training in lung cancer surgery through the metaverse  was implemented in a smart operating room19. Other recent publications demonstrated how AI could be used to identify a surgeon’s skill22 and thus improve continuous learning. Integrating AI in medical (student) education may not only enrich the teaching and learning experience, but may also help to teach opportunities and challenges of AI, and thus results in more awareness, trust, and better use of AI-based systems.AI as well as augmented and virtual reality offer unique opportunities for medical training and educationIn many cases, CDSS demonstrated a better outcome if AI algorithms collaborated with physicians. However, integrating CDSS into the daily clinical routine using real-world data requires rigorous clinical validation in a real-world environment before implementation in clinical practice. Key challenges include trust, bias, scalability, and deployment. Further, regulatory and privacy issues need to be addressed.


The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context.We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education.For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data.The qualitative content analysis led to the formation of three main categories  with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one’s related competencies.The analyzed expert interviews’ results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula. Artificial intelligence (AI) has been of broad scientific interest in medicine for over a decade. This is reflected in the publication of more than 18,000 scientific publications mentioning AI-related terms in that time. AI is expected to revolutionize health care systems around the world. Apart from the economic benefits, AI is expected to make health care more efficient for both patients and health care professionals .With increased public and scientific interest, research into the potential challenges of AI is becoming more commonplace. Recent developments in the use and handling of algorithms in AI applications have raised highly relevant ethical concerns that need to be addressed, in addition to crucial questions regarding patient safety and data . These iResearchers propose that addressing potential challenges regarding the use of AI in medicine requires adequate knowledge of the technology ,6. FurthTo prepare future generations of physicians for the use of AI within the rapidly changing health care system, education needs to adapt to the new challenges. As the development of new curricula modules and teaching content is a time-intensive and complicated process due to traditional structures and accreditation procedures, significant research is needed to define relevant competencies and teaching content regarding AI in medicine.AI has been a topic of interest in computer science since the 1950s . HoweverA distinction can be made between so-called strong AI and weak AI. “Strong AI” defines an AI whose intellectual abilities are comparable to those of humans . HoweverThe application areas of “symbolic AI” in medicine mainly include rule-based expert systems, where the rules to be followed by the AI have been previously defined by experts. Clinical decision support systems can be used in patient care, for example, to support doctors in diagnosis and treatment . The subThe present publication is based on the definition of “weak AI” with its subdomains and all results should be interpreted against this background.The study was conducted to explore essential knowledge and understanding regarding AI in medicine, relevant to define curricula teaching content within medical education. The results should provide the foundation for the improvement of the education of medical students and the medical curriculum.The following section of this study aims to provide a detailed description of the study design, data collection, and data analysis techniques used in this research. The methods used in this study were chosen to ensure the validity and reliability of the results and to ensure that ethical standards were met.The study, conducted from September to November 2022, aimed to identify relevant knowledge and understanding of AI-related teaching content in medical education using semistructured expert interviews. From the total of 68 initially identified and contacted experts in the field of AI in medicine and health care , we were able to include 12 in this study. Most experts were based in Germany (n=10), with 2 experts being included from Austria. For the qualitative data collection, we defined experts as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years.Experts were recruited by email and personal recommendation by the participants. Of the total of 12 included experts, half were primarily working in the field of research and practical development of AI-based applications in the field of medicine . The remaining 6 experts were primarily associated with teaching and research in the field of medical informatics, AI, and digital medicine as part of the medical curriculum . As the experts were primarily recruited by email, an email address that was not publicly accessible through a web-based search was an exclusion criterion.Additional exclusion criteria were no or less than 5 years of experience in the field of AI in medicine, a lack of consent to the transcription or voice recording as well as a missing current or recent involvement in projects related to the research, development, or teaching of AI in medicine.The Research Committee for Scientific Ethical Questions (RCSEQ) of the UMIT TIROL – Private University for Health Sciences and Health Technology, Hall in Tirol, Austria, granted ethical approval for the study.Web-based interviews were conducted, using the Cisco Webex Meeting application. The meetings were recorded using an analogous voice recorder. We obtained consent from the participants before conducting the interviews, including their agreement to be recorded and their data to be used for research purposes. As part of the interview, a semistructured guideline was used. The guideline included questions about the experts’ education and experience in AI, the anticipated impact of AI in medicine, as well as key competencies required for use of AI in medicine, and possible teaching content (please see the supplementary information for the interview guideline). On average, the interviews lasted for 35 minutes.The recorded interviews were transcribed manually with the help of the transcription software f4transkript and a transcription service provider was used to transcribe some of the transcripts. Transcription followed the established rules of Dresing and Pehl . To analAs a result of the qualitative content analysis, we defined 3 main categories  with a total of 9 subcategories. Each of the subcategories is defined by quotes from the participants to highlight the procedure and the original meaning. An overview of the 3 main categories with all associated subcategories is shown in Based on the results of the qualitative content analysis, the first main category was defined. Given the interdisciplinary data collection, the “knowledge” main category summarizes suggested knowledge, which medical students should learn regarding the topic of AI in medicine as part of their education.The first subcategory “basic understanding of AI” highlights the need for basic knowledge and definitions, without an in-depth understanding:But that's not, in my opinion, about people really understanding the technology down to the smallest detail and being able to implement and train on things themselves. I don't think they need that.Interview 7The second subcategory “statistics” relates to the good statistical knowledge needed to understand AI, which was mentioned by half of the experts.The basis is statistics. (...). So that's the basis, because these learning AI methods are all based on statistics.Interview 5This subcategory should also account for the importance of understanding probabilities and their application within medicine. Especially with AI-based applications, statistical knowledge will play a key role in the interpretation of results, which will be further addressed in the second main category.Half of the interviewed experts mentioned the need for an understanding of ethical competencies related to the use of AI in medicine, which is captured in the third subcategory “ethics.”And then just ethical competencies and I think that has a high requirement (...)Interview 10The use of AI-based applications in medicine requires adequate ethical competencies to address the new challenges arising through the interaction with patients and the usage of their data. This does not only refer to the well-known “black-box” phenomena of deep learning or potential bias through unrepresentative training data but rather addresses the topics like the medical self-imagine or the physician–patient relationship too. Although ethics has a long tradition within medical curricula, it also needs to adapt to new technological developments in medicine to address associated challenges and discussions.The last subcategory “data protection and regulation” of the first main category summarizes the need for an understanding of data protection laws and regulations concerning the use of AI in the clinical context, mentioned by 4 of the interviewed experts.(...) where we have to have a good idea of how we can use it, but also what the legal limitations of the whole thing are.Interview 10The need for an understanding of data protection laws does not only apply to the use of AI in medicine but is of increasing significance due to the accelerated digitalization of medicine. An understanding of the regulation regarding the use of AI in medicine can help to prevent uncertainties and potential disapproval by users.The second main category “interpretation” accounts for the high importance to interpret and evaluate the results provided by AI-based applications in medicine. This main category summarizes the statements related to the evaluation of results and should highlight the importance of sufficient knowledge and competencies needed to address all associated challenges.The first subcategory “critical reflection” addresses the need for adequate knowledge and understanding to question the results yielded from AI-based applications critically.(...) also of the possibilities to critically question these things.Interview 4The ability to critically reflect and question the results shows the importance of adequate teaching of content relating to AI in medicine. As with any traditional technology or application, AI-based applications are not free of mistakes, which in the clinical context can have significant consequences.As users need to be aware of potential consequences and risks associated with the results provided by AI, the second subcategory “associated risks” reflects the answers of 5 of interviewed experts:(...) also what are the, yes, risks? What can go wrong? Well, the AI also makes mistakes, of course.Interview 2One of the most mentioned risks was related to false-positive results provided by AI. Without any critical questioning of the results, this can lead to unnecessary treatments for the patients. Although this might be of minor significance in the case of additional physical examination, it could lead to additional exposure to radiation or punctations. Although false-positive results can lead to more imminent negative consequences, the mentioned consequences of false-negative results can be of major significance too in case a disease is not recognized and treated. False-negative or positive results highlight the need to be aware of the associated risks related to the results of AI-based applications in medicine. Furthermore, critical reflection of the results is not only connected to potential associated risks, but rather to an understanding of the data that were used to train AI applications.The third subcategory of the second main category “data basis” represents the statements of 4 of the experts and describes the need for a good understanding and reflection of the data used in the development process of the AI-based application.And, of course, you also have to think about the data that might be fed into it now, do they make sense? Are they representative?Interview 2Both are important requirements to interpret the results and are closely associated not only with the other subcategories of this main category but rather with the subcategories from the first main category too. Without a basic understanding of statistics and how AI-based applications work, it is hard to understand the need for representative data samples. Potential bias makes ethical competencies necessary to interpret and critically question the results based on the data basis. This subcategory does not only refer to the need for an understanding of whether the data basis is representative of the current patient, but rather the imminent need to understand that current AI applications have very narrow use cases. To prevent false diagnosis and associated consequences, it is necessary to critically reflect on the unreliable results that can arise from deviation from the specific use case.Analysis of the interviews yielded a third main category named “application.” This category comprises 2 subcategories and summarizes the requirements to apply AI-based applications in clinical practice.The first subcategory “practical skills” addresses the practical skills required, to use AI-based applications of any kind.In clinical practice, the most important thing is actually the practical application.Interview 1This subcategory further includes basic technological understanding and skills needed, to apply any software application. Based on the feedback from half of the interviewed experts, this includes for example competency to use hardware like desktop computers, including keyboard and mouse or operating software used in the clinical context. Moreover, this subcategory summarizes the knowledge and understanding needed to apply AI software within the clinical workflow. Users need to understand whether it makes sense to use the applications and how they can be used to improve the workflow in clinical practice.The second subcategory “trust” represents a base layer needed to use any technology. This subcategory relies on adequate knowledge (first main category) and teaching within the medical curricula. The absence of teaching as part of the medical curriculum could further lead not only to the lack of trust and potentially the disapproval of the application, but could also lead to a blind trust, which can have significant consequences as part of the interpretation of results.Creating trust, but not blind trust.Interview 12Creating trust not only concerning the use of AI-based applications but rather trust regarding the own competencies in the process of applying AI-based applications within the clinical context is one of the challenges that can be addressed as part of medical education.The results indicate the significance of the integration of teaching content regarding AI as part of the medical curriculum. All experts interviewed agreed on the importance of teaching AI content in the medical curriculum, which echoes the current state of literature ,8,19. AlThis agreement is reflected through the definition of the 3 main categories . Most experts recommended that medical students should only receive basic knowledge of current AI models and terminology, as they will not be required to develop or train AI-based applications themselves, which is also in line with recommendations of current publications ,20. HoweThe practical challenges and barriers of implementing new teaching content, such as the need for the renewal of accreditation or sufficient knowledge of the teaching staff, further reinforce the recommendations of the experts to only facilitate a basic level of knowledge acquisition of AI as part of the medical education . The expFor many of the interviewed experts, the ability to interpret results provided by AI applications concerning the data basis and the associated risks is highly important when it comes to preferred teaching outcomes. The results from this study confirm the imminent need for an early and conscientious implementation of curricula teaching content on AI, as suggested by earlier studies ,26,27. FThe experts’ statements reveal a disagreement and lack of standardization in the definition of AI. Recent publications on the integration and teaching of AI within medical education commonly lack a specific and dedicated definition of AI ,8,19. GiThe need for standardization in the definition of AI as a foundation for related teaching content is further emphasized by the potential ethical challenges and issues that may arise from the use of different types of AI in a clinical context. For example, in the context of bias, clinical decision support systems can be subject to bias arising from the unintended transfer of existing bias on the part of the developers ,32. FocuAlthough the integration and teaching of AI as part of medical education have been of increased scientific interest in recent years, further highlighting the need for early and adequate education of medical students, the available research is still limited ,8,19,34.The results of this study highlight the need for comparability, as the experts’ statements not only confirm the results of current literature but further specify and highlight the importance of awareness of associated risks, critical questioning of the results, as well as the significance of basic technical and technology skills ,25,36. FThere are several limitations of this study. Using qualitative research methods, the level of generalization is limited due to a small sample size. Although we sought an interdisciplinary approach to the data collection, the results of the study still represent the subjective opinions of the participants. Furthermore, the results are likely to be subject to a selection bias, as no randomization was used and participants were recruited through recommendation. As only a limited number of standardized questions within the data collection were used, interviewer’s bias is also possible. Additionally, as the data collection was conducted through a web-based service provider, technical difficulties may have affected the quality of the collected data.This study aimed to explore and define relevant knowledge and understanding concerning the subject of AI in medicine as part of the medical curriculum. The results of the study, based on qualitative content analysis of expert interviews, indicate that knowledge and understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation should be part of medical education. Furthermore, medical students need to be able to interpret and critically reflect on the results provided by AI, considering the associated risks and data basis. The development of trust in AI as well as the acquisition of related practical skills, including the need for basic technological skills, should be an indispensable part of medical education.As AI in medicine is likely to become increasingly significant in the future, medical users will need adequate knowledge and understanding to use it effectively. Due to the new opportunities and challenges associated with the use of AI-based applications in medicine, medical education needs to adapt to those changes, to provide future generations of physicians with the necessary knowledge and competencies. The research aims to emphasize the importance of integrating teaching content related to AI into the medical curriculum. The results provide implications for the creation of new teaching content based on interdisciplinary data collection. Furthermore, the results further imply a need for standardization in the definition of AI as a foundation for associated teaching content and the integration of AI into medical education. Subsequent research should explore the practical implications of this study and how the results can be transferred into the medical curriculum. Furthermore, research and the development of tools are needed to assess the current knowledge and competencies of medical students regarding the use of AI in medicine. This will not only have practical implications for the creation of new teaching content but will rather allow an assessment of the success of new teaching content in the future.


Factors of Perceived Trustworthiness from Mayer, Davis, and Schoorman’s Model of Trust and made propositions to define AI’s role in trustful scholarly communication.In this article, we reflect on the pros and cons of artificial intelligence (AI)-augmented scientific writing for more comprehensible research towards society to gain trust for science-led policy. For this purpose, we integrated our thoughts into the  Letter to the editorIn ancient Greek drama, the deuteragonist is the second actor in the play. While interacting closely with the protagonist to support and assist, the deuteragonist can also assume a role of conflict or rivalry; ultimately helping to develop the main character and shape the overarching narrative of the play .Since artificial intelligence (AI) came to the stage in 1956 , it has Balancing the benefits and risks of AI in scholarly communicationThe concept of AI assisting researchers in scientific writing is appealing. If executed correctly, AI could support more comprehensible research and increase acceptance of science-led policy by the general public. However, concerns have been raised about issues such as plagiarism and obscurity in research, particularly with unregulated use of AI-driven scientific writing assistance (SWA) and chatbots ,18. In tThe prosSWA has the potential to make research more accessible and understandable by facilitating core findings for wider target groups, including non-scientific stakeholders, policymakers, and the public (benevolence+) . In addiAI can generate various visual representations of research, ranging from simple semiotic visualizations to more complex schematic views. These offer effective and easily comprehensible data communication for other researchers and a broader audience in a non-textual form (ability+).Humans are prone to errors, especially when performing repetitive and monotonous tasks like formatting citations and proofreading . AI autoAI can quickly analyze and summarize vast amounts of scientific literature or content data, enabling researchers to uncover patterns, correlations, and trends not easily identifiable through manual analysis alone (ability+). This can also contribute to improve the overall quality of the research process; for example, in identifying relevant research questions and interpreting data, resulting in a more targeted design of original research and literature reviews (benevolence+) ,27.The cons and possible measures-). This should be addressed by fostering the establishment of balanced training of datasets, bias detection, and correction algorithms, as well as close human supervision and feedback mechanisms [Since AI chatbots are based on large language models (LLM), their answers are always a function or a derivate of their repertoire and hold a risk of bias to scientific writing, based on possible biases in the training datasets themselves. This may include racial, sexual, or religious bias, making the results less inclusive -30 . Mitigating such concerns must be enforced by public dialogue, addressing integrative scenarios including supervising, and collaborating definitions as well as adequate education and reskilling programs for the people in an increasingly AI-automated world.Automation of tasks, e.g., the aforementioned SWA, may stoke fear of job loss  . This could be closed by the aforementioned human-AI collaboration, as well as pairing LLMs with augmented reality, evolutionary algorithms, generative models, fine-tuning, and zero-/few-shot learning to generate inclusive novel ideas and writings [The crystalline nature of LLMs precludes chatbots from actuality. More importantly, while they are neither living nor experiencing culture, LLM chatbots are put into an insurmountable epistemic distance toward the user’s world of thought, which ultimately makes current chatbots incapable of generating a priori ideas or conclusions for society (integritywritings -36.-), lowering people’s trust in it, and open the door for predatory journals to misuse AI to mass-produce low-quality, inaccurate, and misleading content, based on monetary interests [-). We suggest the encouragement of education and training in young researchers on critical thinking and writing skills, highlighting that AI is a tool of assistance, not a replacement for the human intellect [Overreliance on AI for writing tasks can lead to a decline in critical thinking and writing skills among researchers, as well as an impoverishment of creativity and inclusivity. This could potentially jeopardize the integrity of scientific research . We advise fact-checking and the use of verification systems to confirm the accuracy of AI-generated text before publication or use, predominantly by human reviewers, but also other AI-based systems, as well as the development of AI algorithms specifically designed to detect and correct hallucinations and other errors in AI-generated text [Lack of explainability  and the ted text .-). Additionally, access to AI tools may be limited due to factors such as cost or institutional constraints . This could be counteracted by faculties by providing comprehensive training to researchers on the use of AI tools, ensuring that they are comfortable and proficient in their application.Implementation and effective utilization of AI in scientific writing requires technical expertise and resources. Not all researchers may have access to or be comfortable with using it, potentially creating a split in the scientific community  ,35, it iLimitations of AI for Scientific WritingThe inherent risk of introducing bias, the phenomenon of "hallucination," a lack of originality, limited accessibility, and insufficient transparency and explainability hinder AI from becoming a protagonist in scientific writing at its current technological stage and without clear guidelines.For now, we advocate the general disclosure of AI usage in the methods section of research articles as an initial step. The declaration should include the type of AI used and clarify the role the AI had in the creation of the manuscript. In addition to that, the corresponding scripts and prompts, as well as chatbot parameters, should accompany the article as supplementals and data repositories. To guarantee full transparency, the following measures should be developed and established: (a) standardized reporting frameworks customized to the AI model in use. This ensures that essential AI-related details are reported consistently and thoroughly, providing additional clarity to the reader. (b) Introduce a visual cue on the published article, in analogy to open access labeling, by creating an international AI usage badge Figure ; a visuaConclusionsThe boundary that separates AI as a mere tool and a potential autonomous contributor - a protagonist, akin to a coauthor - gradually becomes less distinct as its technology and usage evolve. However, the future role of AI as a protagonist in research has to be postponed. For now, AI will remain in the second line, making us protagonists better authors through its help and our consecutive reflection on the rules of good scientific writing for a more comprehensible and trustful scholarly communication.