## 4 Discussion
### Highlighted Impressions and Overall Themes
#### 4.1.2 Real-World Clinical Implementation, including Diagnostics, Education, and Technology  
A well-represented topic in this collection is the application of AI to various aspects of healthcare, particularly within the realms of predictive models, diagnostics, medical education, and technology, including telemedicine and devices. The utility of AI in clinic implementation has been extensively applied throughout the past decade. However, novel methodologies, including large language models, better system architecture, and new regulatory procedures, have decisively created a shift into more real-world applications. The use of AI as a prognostic or diagnostic screening tool is not something new, and there are many studies showing its utility in fields like radiology, pathology, and beyond. The COVID-19 pandemic, however, revealed the potential of these tools in challenging situations. During the pandemic, health systems were unable to operate at maximum efficiency due to issues like overcrowding and resource limitations. Therefore, having systems to help synthesize and analyze data to output initial diagnoses has shown promise to help clinical practitioners make more rapid yet informed diagnoses, particularly for COVID-19. One study in this list presented an early AI algorithm that analyzed CT images for COVID-19 diagnosis and achieved a robust performance of 89.5% accuracy with 0.88 specificity and 0.87 sensitivity on internal testing and 79.3% accuracy with 0.83 specificity and 0.67 sensitivity when tested on an external dataset [20]. This performance hinted at the potential for offering a rapid, reliable alternative to traditional pathogen detection methods. Another study further advanced this domain by developing a deep learning-based CT diagnosis system that not only distinguishes COVID-19 from bacterial pneumonia with high accuracy but also identifies critical lesion features, enhancing diagnostic precision [22]. The third study employed a DenseNet201-based deep transfer learning model to classify COVID-19 cases using chest CT images [17]. This approach demonstrated superior performance over methods that existed at the time of the publication of their research. Together, these studies underscore the possibilities of AI-enabled diagnostics in times of pandemics. Another recently popular trend in the field of biomedicine is the utility of telemedicine, which represents remote care utilizing technology like video conferencing and chatbots. Telemedicine offers accessible and efficient medical services, especially in remote or underserved areas, utilizing observations made from patient images or videos taken from a smartphone. Telemedicine can also incorporate AI analysis of such remotely taken images for initial screening and diagnostics, which can be used to determine if an in-person visit is recommended. One such work represented in this list explores the power of telemedicine within the field of ophthalmology on a global scale [18]. This review illustrates how digital innovations can enhance remote medical services, a need that became particularly pressing during the COVID-19 pandemic. It showcases AI's role in screening eye images of patients unable to routinely visit ophthalmologists in person, overcoming geographical barriers, and making decentralized delivery of certain eye care recommendations possible, thus broadening the accessibility of specialized medical services. The last subtopic represented in this category is the role of AI in medical education. Medical training is complex and dynamic, and it is now more rapidly changing given the swift advancement of research and online dissemination, significantly outpacing the traditional timelines of medical education and knowledge acquisition. One study evaluated how ChatGPT can aid in medical training, offering a new dimension to learning that is interactive, up-to-date, and highly accessible. This is especially vital in the rapidly evolving field of medicine, where keeping abreast of the latest information is crucial [27].