dataesr/scientific-paragraphs-categorization huggingface dataset:
I. INTRODUCTION About 10% of global gross domestic product (GDP) (10 trillion USD) is spent on healthcare annually  [1]. The recent advancements in technology, especially data-driven methods and computational processing power can benefit, both the patients and the medical industry, as well as reduce the huge expenditures. Moreover, massive healthcare data is available from sources such as; electronic health records (EHRs), genomics profiles, medical imaging, chemical, and drug databases. Analytical methods, especially deep learning-based Artificial Intelligence (AI) methods, can provide the tools to design useful clinical and medical applications to process these large datasets. Data-driven methods could offer benefits in medical record digitization, clinical trials, diagnosis assistance, prognosis evaluation, and the design of optimal prevention and treatment strategies, as well as precision medicine, drug discovery, and health policy.
Advances in computational infrastructure have provided the capacity to generate, store, analyze and visualize large, complex, and dynamic datasets typical of modern biomedical studies  [2]. New treatment options are being developed and tested in clinical trials  [3]. In the last decade, artificial intelligence has moved from theoretical studies to real-time applications thanks to the rise in the computational capacity of GPUs and TPUs. Methods like AutoML  [4]  and explainable artificial intelligence (XAI)  [5]  are advancing, which have the potential to transform the current medical practice. However, there are still many bottlenecks to realizing the full potential of analytical methods in the healthcare industry. Important challenges for data science in medicine include data collection, standardization of data formats, missing data values, developing large and efficient computational infrastructure, data privacy and security, and others.
The interrelated nature of biomedical data is one of its most important properties. Such data can be represented in the form of graphs. Graph machine learning allows for the modeling of unstructured multimodal datasets. Graph machine learning can model more complex relationships between disease and patients, understand tumor micro-environment, predict drug response, and re-purposing. Additionally, graph machine learning coupled with attention mechanism may provide more interpretable machine learning models than typical traditional black-box models. The recent breakthrough of the artificial intelligence (AI) system Alphafold2  [6]  in predicting the three-dimensional structure of proteins solely from the amino acid sequence is a huge success. AlphaFold2 won the Critical Assessment of Structure Prediction (CASP)  [7] , the worldwide event for protein structure prediction, since 1994.
Meta AI also joined the race and developed an AI system to predict structures of about 600 million proteins  [8]. However, how to translate this into the in vivo situation is still an open question. AlphaFold2 can predict unbound protein structures; however, most practical applications require protein-drug complex predictions. There have also been significant advancements in processing power and biosensor technologies. For example, with the help of parallel processing methods and powerful GPU clusters, such as NVIDIA-DGX, we can now process massive complex multi-dimensional biomedical datasets  [9]. Moreover, wearable electronics, such as electronic tattoos (Etattoos), epidermal electronics systems (EES), and flexible electrochemical bioelectronics, coupled with machine learning algorithms can be used to monitor various biomarkers in real time  [10].
B. AI TOOLS AND COMPANIES FOR CLINICS The development and use of computer aided diagnosis or AI tools in clinical practice confront several hurdles despite the huge advancement in this new age of machine learning. For example, medical imaging  [47]  is an essential diagnostic tool for various disorders. A variety of imaging modalities have been developed, with X-ray imaging, whole slide imaging, computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), and positron emission tomography (PET) being some of the most widely utilized techniques. Moreover, several publicly available imaging and biological databases also offer excellent opportunities to build AI systems. For example, PathAI  [48]  uses AI methods to assist pathologists in clinical diagnostics, clinical trials, and clinical translational research.
1) SaMD: SOFTWARE AS A MEDICAL DEVICE SaMD  [51]  is meant to be used for one or more medical purposes and is not part of physical medical equipment. Since 1995, more than 500 software packages/applications have been approved by the FDA to assist doctors in various healthcare problems  [52]. Most of these software packages are related to analyzing radiology images. In many medical imaging tasks, AI algorithms have outperformed humans, and innovative companies have built AI-based systems to analyze radiology images and digital pathology slides. For example, Chan et al. [53]  created a computer-aided diagnosis system to identify micro-calcification on mammograms and carried out the first observer performance research that showed how well the developed tool improved breast radiologists' ability to detect micro-calcifications.
IV. APPLICATIONS OF BIG DATA ANALYTICS IN HEALTHCARE The healthcare system consists of multiple stakeholders; patients, doctors, hospitals, industry, and policymakers, which are regulated by strict compliance. Healthcare systems generate a huge amount of data at a very high speed, which makes it a perfect avenue for big data analytics. Using big data analytics in healthcare may enable personalized medicine, timely interventions, better health policy management, and planning  [65]. Big data analysis systems aim to collect, clean, extract, visualize, and analyze very large datasets and are associated with three key concepts. These are volume (large datasets), variety (highly dimensional/many attributes), and velocity (the speed at which the data is generated, made accessible, and analyzed). Healthcare datasets, usually large, complex, and arising from various sources, offer valuable opportunities for big data platforms  [66].
Centralized and mobile applications for patients, clinicians, hospitals, government agencies, and global health organizations can be developed. For example, the FDA has approved Ziopatch  [68] , which measures the heart rate and the ECG signal. Multi-variate statistical methods, such as principal component analysis and other clustering methods, can be used to find patterns in a big dataset that may identify different disease states, mortality rates, susceptible age groups, forecast future pandemics, and economic costs  [69].
B. GENOMICS DATA ANALYSIS Genomic datasets, facilitated by next-generation sequencing, often contain vast amounts of raw data  [80]  and require big data analysis and computational methods. Examples are the encyclopedia of DNA elements (ENCODE)  [81]  gene annotation and expression data, the Cancer Therapeutics Response Portal (CTRP)  [82] , which can provide insights into the action of small molecules leading to personalized drug discovery based on predictive biomarkers. The Cancer Cell Line Encyclopedia (CCLE)  [83] , and the Genomics of Drug Sensitivity in Cancer (GDSC)  [84]  database of large scale molecular screens on panels of hundreds of characterized cancer cell lines demonstrates the potential of modern machine learning algorithms to develop drug response predictors from molecular profiles.
C. MEDICAL IMAGING Deep learning can rapidly construct magnetic resonance (MRI) images directly from sensor data of partially observed measurements. Task-oriented reconstruction allows the reconstruction of a specific part of the image with high quality and a confidence score. Super-resolution images (high-quality images or sequences built from low-resolution images) can be constructed by deep learning, such as single (no reference information) brain MR images built using convolutional neural networks (CNNs) or super-resolution using GANs  [87]. In Figure  4 , we show various applications of deep learning in medical imaging. For MRI images, image synthesis is a method to generate new parametric images or tissue contrasts from a collection of images acquired in the same session. Generative adversarial networks  [88]  could serve as a data augmentation tool as medical datasets tend to have limited numbers of samples, and they have been used to generate synthetic abnormal MRI images for a brain tumor based on pix2pix  [89] ,  [90].
A. ACCELERATED ARTIFICIAL INTELLIGENCE Deep learning systems are often trained on multiple core graphical processing units, which can optimize the highly parallel matrix operations that are essential components of deep neural networks. A recent example is the discovery of faster matrix multiplication using reinforcement learning  [114]. Google introduced a tensor processing unit (TPU) as an accelerated artificial intelligence processor, especially for its TensorFlow software  [115]. Training of a deep neural network can be expedited by either training more examples in parallel or training each example faster. Operations that cannot be accelerated by GPUs or TPUs, such as the earlier data processing stages or input-output between devices or disks, need to be improved in training. Data echoing  [116] , which reuses intermediate outputs from earlier pipeline stages to reclaim idle capacity, may be useful to ameliorate this.
B. EDGE COMPUTING Although most healthcare datasets are complex and large and require massive computational resources (often in remote computer clusters), processing data locally at the end nodes of a cluster in a real-time application is appropriate for privacy reasons or to reduce processing time and latency. The training of the model locally on end nodes is known as edge computing. In edge computing, edge (local) devices or servers can provide data storage and processing, potentially giving fast, secure, and real-time health analytics that may allow timely medical interventions. Thus, an edge computing-based AI model could provide better healthcare for patients far from major population centers with limited connectivity and access. The localized processing power of edge computing may facilitate access to medical interventions by rapidly analyzing data from smart medical sensors.
C. FEDERATED LEARNING Data privacy and protection are general requirements for medical data, and new frameworks for training models are required that do not expose the underlying data. One such approach is Federated or Collaborative Learning  [122] , which is a machine learning technique that trains an algorithm across multiple edge devices or servers without exchanging local data samples. Multiple parties, for example, several hospitals/research centers, actively collaborate to train algorithms without centralizing their datasets. In developing AI models for medical data from multiple locations, federated learning has recently been shown to be effective. For example, with the rapid spread of COVID-19 globally, researchers needed to come up with quick responses and rapid developments of mechanisms for the assessment of COVID-19 patients.
A perspective about sharing biomedical data for strengthening the role of AI is presented in  [127]. Machine learning methods can also assist in automated labeling, anomaly detection, missing value imputation, and other data cleaning processes  [128]. For example, in  [129] , deep learning is used to identify bleeding events from electronic health records. Deep learning models are frequently used to improve the quality of radiology or pathology scans  [130]  or to identify anomalies in biosensors  [131]. Some IT vendors also provide automated scrubbing tools that use logic rules to compare, contrast, and correct large datasets. Another issue is the widespread perception in the community that larger datasets are required to make accurate predictions. The data quality, proper annotations, and hypothesis in consultation with healthcare experts are necessary to build robust machine learning models.
1) THE CHALLENGES IN DISTRIBUTION SHIFTS AND DIFFERENT DATA MODALITIES Many real-world clinical AI systems suffer from the training and testing distribution shifts in the data. To deal with these distribution shifts, domain adaptation techniques are adopted in machine learning. In domain adaptation, we train a neural network on a source dataset X and achieve high accuracy on a target dataset Y, where X and Y have different data distributions. Domain adaptation can be sliced down into three categories: supervised, semi-supervised, and unsupervised learning, depending on the type of data from the training dataset. In supervised fast-expanding target dataset is substantially smaller than the source dataset since the target domain's data has been labeled. While unsupervised learning makes use of unlabelled data from the target domain, semi-supervised learning uses both labeled and unlabelled target domain data.
Finally, considering the size of the target dataset and how closely it resembles the source dataset, the initial layers can be fine-tuned or frozen during the training procedure. Another deep domain adaptation  [135]  technique, reconstruction-based domain adaptation, uses an autoencoder to reduce reconstruction error and learn transferable and domain-invariant representations to align the discrepancy between domains. Stacked Auto Encoders (SDAs) can be used to represent source and target domain data in a high-level representation manner  [136]. However, because SDAs are computationally expensive, the marginalized SDA (mSDA), which does not require the use of stochastic gradient descent, was presented in  [137]  to overcome the computational cost. Transfer learning with deep autoencoders (TLDA)  [138]  used a softmax loss to encode the source domain's label information.
2) CHALLENGES IN MEDICAL IMAGING Perhaps, medical imaging is the most disruptive area where AI has made tremendous progress. However, there are various challenges in medical imaging as well  [142]. Medical images are often three-dimensional, and the three-dimensional convolutional neural networks to process these 3D volumes require more memory and computational time. Generally, researchers treat 3D CNNs as stacks of 2D CNNs. However, adding a newer dimension adds additional constraints. Most deep learning models are built on anonymized public data, making privacy-related issues less relevant. However, this does not offer a permanent solution to handle privacy-related problems in medical imaging. One conclusion is that when these datasets are made public, there are always associated risks of leaking patient privacy  [143].
For example, algorithms with similar performance may use different strategies to solve the same problem, necessitating special pre-processing techniques before inference. As a result, scalability, which is critical in commercial AI-based products, becomes difficult because each application may require its own server or virtual environment. The transferability of the algorithm presents another challenge due to the stringent medical regulations in different nations. However, there is no statistical method available to evaluate an algorithm's transferability. One such initiative is the petabyte 'medical-imagenet' project of radiology and pathology images by Stanford University with genomics and electronic health record information for rapid creation of computer vision systems(Stanford-AIMI). The challenge of a lack of large datasets can be addressed by image synthesis and data augmentation.
5) BIGDATA IN SMART SENSORS Establishing a smart sensor system that relies on enormous datasets and algorithms, is a significant barrier regarding the platform for data processing and storage. In recent years, cloud computing has been used to process sensor signals since it offers superior computational power and data storage. Cloud and biosensor integration is nothing new, especially for monitoring applications where the volume of data is continuously growing over time. The direct connection of many sensors to the cloud is sometimes too expensive and sluggish due to the exponential growth in the number of sensors. Edge computing has so been introduced in recent years. Instead of a single data centre, edge computing enables data processing at scattered edge devices. It benefits from great computational effectiveness, rapid network processing, low cost, and more.
C. LARGE LANGUAGE MODELS FOR HEALTHCARE While the development of Large Language Models (LLMs) has been the focus of researchers  [151] ,  [152] ,  [153]  for a while relating to application towards machine translation, text summarizing and paraphrasing and generation of text, the recent release of ChatGPT  [154]  from OpenAI has brought the potential use of chatbots into mainstream consumer use. LLMs are deep learning models trained on a large amount of textual data to cater to multiple tasks related to Natural Language Processing. LLMs make use of complex transformer architectures that enable it to capture longer dependencies than is possible with typical sequential models such as RNNs. LLMs also have the advantage of being able to be fine-tuned for specific tasks, thereby performing well in some desired niche or even work as the backbone for generic chatbots too with a fine tuned performance.
Infact, Open AI's GPT-3 has been used as the back-end of several such offerings, including JasperChat (tailored for business use) and Poe by Quora, both of which are based on OpenAI's base models. The multifaceted use of LLMs for special domains has also been true for the case of healthcare, medical data, as part of the used training data corpus enables chatbots powered by LLMs to be useful in assisting healthcare practitioners. One such way this was performed was suggested by Wang et al. [155]  who incorporate LLMs in to a CAD system for medical images called ChatCAD. They do this by generating prompts based on the output of different image based classifier/segmentor and report generator. These outputs are converted in to a prompt and are then passed on to the LLM so that its logical reasoning capabilities could be used to provide better and interactive care to patients.
1) ChatGPT FOR HEALTHCARE The OpenAI's language chatbot ChatGPT  [154]  is an artificial intelligence language model that has been pre-trained on a large corpus of text data and is capable of generating human-like responses to natural language queries. Having passed successfully part of the US medical licensing exam, attesting to its capability to work with medical queries, Chat-GPT has the potential to revolutionize clinical applications in many ways  [156]. In Table  3 , we enlist several applications of ChatGPT.
The ARMS code uses the PARAMESH toolkit  (MacNeice et al. 2000 ) that provides parallel adaptive mesh refinement to adapt the grid throughout the computation to the evolving solution. Five levels of grid are allowed during this simulation. Each grid block consists of 8 × 8 × 8 grid points. Initially, the grid is equally spaced in Θ and Φ and stretched exponentially in radius (see Fig. 1  right). In order to resolve the flows and the gradients resulting from the photospheric forcing, we allowed the grid to reach the maximum level of refinement (level five) in the region that encompasses the studied magnetic structure. The maximum level is also reached in the regions of high electric current in the pseudo-streamer, around what we later call the closed-separator (see Sect. 2. 3 for a description of magnetic topology and Sect.
Acknowledgements. T. P. F. , S. M. , and E. P. acknowledge the support of the  APR program  of  CNES , focused on the  Solar Orbiter  mission. This work was also supported by the  Programme National PNST of CNRS/INSU  co-funded by  CNES  and  CEA , and was granted access to the HPC resources of CINES under the allocations 2020 [ A007046331 ] and  2021  [ A009046331 ] made by  GENCI. We thank the  Alliance Hubert Curien Program  funded by the  Ministère de l'Europe et des Affaires Étrangères  and the  Ministère de l'Enseignement Supérieur et de la Recherche  and managed by  Campus France. P. F. W. was supported by  STFC (UK)  consortium grant  ST/W00108X/1. C. R. D. was supported by  NASA H-ISFM  and  H-SR research  grants.
Methodological Approaches and Data Collection For the exploratory study, we constructed a semistructured interview guide that allowed us to collect the following information: 1. Description of the current procedure of a cardiac arrest call (eg, "Can you describe how a call is taken?") 2. Perceived benefits of and barriers to using an app to send emergency actions (eg, "In what situation do you think it would be beneficial to use SARA?") 3. Dispatcher needs (eg, "Would there be any other apps that you perceive as useful? Why?") We conducted 5 interviews (with a mean duration of 24. 14, SD 6. 05 minutes). During the pretest, 1 scenario was tested: "You find a 60-year-old unconscious man in Chaumettes Park. You call 144 and follow the instructions the dispatcher gives you. " The dispatcher assesses the patient via audio communication.
Data Analysis First, we transcribed all the audio recordings (interviews and debriefings), which allowed us to obtain a corpus for our studies 1, 2, and 3. In the filmed sequences (studies 2 and 3), we also recorded the times from the moment the dispatcher picked up the phone, including the recognition of the cardiac arrest, implementation of the emergency procedures, transmission of the video, and reception of the video. We then compared our data with those from the literature. We also noted the number of times the applications were not successful. We then categorized the different simulations into action sequences (discovery of the individual in need of emergency care, call for help, 144 call, evaluation of the individual's condition, implementation of gestures, arrival of the first responder, implementation of the defibrillator, and arrival of the paramedics) to compare the situations among them in terms of actions carried out (observations) and feelings (debriefings).
Second, we performed a thematic analysis using ATLAS. ti (version 8. 1; ATLAS. ti Scientific Software Development GmbH) on our corpus. Thematic analysis is a method that "consists of systematically identifying, grouping and, subsidiarily, examining the discourse of the themes addressed in a corpus"  [49]  by categorizing the verbatim accounts according to the themes of the QRs. Thus, for study 1, we had 3 main themes (OHCA procedure, interest in and obstacles to using SARA, and the need for regulators), which we were able to divide into subthemes (eg, for the OHCA procedure: an evaluation procedure, a procedure for implementing the gestures, and difficulties mentioned). Likewise, for study 2, we had 3 themes: the impact of SARA on care (with the subthemes difficulties, obstacles, and benefits mentioned), the benefits for the community (with the subthemes learning, awareness, and confidence), and the subjective evaluation of the method (with subthemes obstacles mentioned, bias, and elements to be modified).
Ethical Considerations We provided a document to all the participants explaining that we were collecting data (photographic, audio, and video) as part of the research and that these data would be used only to analyze the process and as support for scientific presentations. At the beginning of the session, all the participants were asked to read and then sign the consent forms or indicate their opposition before the experiment. The analysis and presentation of the data and their results are anonymized (participants are identified using a coding system). The compensation offered was 1 hour of free training in first aid and emergency procedures by certified professionals. Each participant signed a General Data Protection Regulation consent form, providing permission for audio and video recording and the use of the recordings for research purposes.