Pub Date : 2024-08-24DOI: 10.1016/j.apjo.2024.100094
Madhavi Devaraj, Vasanthakumar Namasivayam, Satya Swarup Srichandan, Eshan Sharma, Apjit Kaur, Nibha Mishra, Dev Vimal Seth, Akanksha Singh, Pankaj Saxena, Eshaan Vasanthakumar, James Blanchard, Ravi Prakash
Background: Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app "Roshni" for cataract screening.
Method: The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm's suitability for field use.
Results: The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.
Conclusion: This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.
{"title":"Development and testing of Artificial Intelligence based mobile application to achieve cataract backlog-free status in Uttar Pradesh, India.","authors":"Madhavi Devaraj, Vasanthakumar Namasivayam, Satya Swarup Srichandan, Eshan Sharma, Apjit Kaur, Nibha Mishra, Dev Vimal Seth, Akanksha Singh, Pankaj Saxena, Eshaan Vasanthakumar, James Blanchard, Ravi Prakash","doi":"10.1016/j.apjo.2024.100094","DOIUrl":"10.1016/j.apjo.2024.100094","url":null,"abstract":"<p><strong>Background: </strong>Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app \"Roshni\" for cataract screening.</p><p><strong>Method: </strong>The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm's suitability for field use.</p><p><strong>Results: </strong>The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.</p><p><strong>Conclusion: </strong>This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.</p>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142071889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100089
Purpose
To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.
Design
A literature review and analysis of current AI applications and educational programs in ophthalmology.
Methods
Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.
Results
Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students’ education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.
Conclusions
Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.
{"title":"A review of ophthalmology education in the era of generative artificial intelligence","authors":"","doi":"10.1016/j.apjo.2024.100089","DOIUrl":"10.1016/j.apjo.2024.100089","url":null,"abstract":"<div><h3>Purpose</h3><p>To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.</p></div><div><h3>Design</h3><p>A literature review and analysis of current AI applications and educational programs in ophthalmology.</p></div><div><h3>Methods</h3><p>Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.</p></div><div><h3>Results</h3><p>Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students’ education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.</p></div><div><h3>Conclusions</h3><p>Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000902/pdfft?md5=c732bd60e43fb37cd5168b2517837774&pid=1-s2.0-S2162098924000902-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141970533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100093
{"title":"Upholding artificial intelligence transparency in ophthalmology: A call for collaboration between academia, industry, and government for patient care in the 21st century","authors":"","doi":"10.1016/j.apjo.2024.100093","DOIUrl":"10.1016/j.apjo.2024.100093","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S216209892400094X/pdfft?md5=e9714c7ce326049bd067f564c27d2d22&pid=1-s2.0-S216209892400094X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142003495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100079
{"title":"Comment on “Update on coronavirus disease 2019: Ophthalmic manifestations and adverse reactions to vaccination”","authors":"","doi":"10.1016/j.apjo.2024.100079","DOIUrl":"10.1016/j.apjo.2024.100079","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S216209892400080X/pdfft?md5=b94554e4ffe0c934547ebaa4c3d03c84&pid=1-s2.0-S216209892400080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141282834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100087
Purpose
Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visualising the important features responsible for predictions. This ultimately improves interpretability and confidence. In this work, we review the use case for SMs, exploring their impact on clinicians’ understanding and trust in AI models. We use the following ophthalmic conditions as examples: (1) glaucoma, (2) myopia, (3) age-related macular degeneration, and (4) diabetic retinopathy.
Method
A multi-field search on MEDLINE, Embase, and Web of Science was conducted using specific keywords. Only studies on the use of SMs in glaucoma, myopia, AMD, or DR were considered for inclusion.
Results
Findings reveal that SMs are often used to validate AI models and advocate for their adoption, potentially leading to biased claims. Overlooking the technical limitations of SMs, and the conductance of superficial assessments of their quality and relevance, was discerned. Uncertainties persist regarding the role of saliency maps in building trust in AI. It is crucial to enhance understanding of SMs' technical constraints and improve evaluation of their quality, impact, and suitability for specific tasks. Establishing a standardised framework for selecting and assessing SMs, as well as exploring their relationship with other reliability sources (e.g. safety and generalisability), is essential for enhancing clinicians' trust in AI.
Conclusion
We conclude that SMs are not beneficial for interpretability and trust-building purposes in their current forms. Instead, SMs may confer benefits to model debugging, model performance enhancement, and hypothesis testing (e.g. novel biomarkers).
目的:显著性图(Saliency maps,SM)通过可视化负责预测的重要特征,让临床医生更好地理解人工智能(AI)模型中不透明的决策过程。这最终会提高可解释性和可信度。在这项工作中,我们回顾了 SM 的使用案例,探讨了 SM 对临床医生理解和信任人工智能模型的影响。我们以以下眼科疾病为例:(1)青光眼;(2)近视;(3)老年性黄斑变性;(4)糖尿病视网膜病变:方法:使用特定关键词在 MEDLINE、Embase 和 Web of Science 上进行多领域检索。结果:研究结果表明,SMs 在青光眼、近视、AMD 或 DR 中的应用非常普遍:结果:研究结果表明,人工智能模型经常被用于验证人工智能模型并倡导采用人工智能模型,这可能会导致有偏见的说法。研究发现,人们忽视了SMs的技术局限性,并对其质量和相关性进行了肤浅的评估。关于显著性地图在建立人工智能信任方面的作用,仍然存在不确定性。加强对突出显示图的技术限制的了解,改进对其质量、影响和对特定任务的适用性的评估至关重要。建立选择和评估SMs的标准化框架,以及探索它们与其他可靠性来源(如安全性和普遍性)的关系,对于增强临床医生对人工智能的信任至关重要:我们的结论是,目前形式的 SMs 对可解释性和建立信任并无益处。相反,SMs 可为模型调试、模型性能提升和假设检验(如新型生物标记物)带来益处。
{"title":"The role of saliency maps in enhancing ophthalmologists’ trust in artificial intelligence models","authors":"","doi":"10.1016/j.apjo.2024.100087","DOIUrl":"10.1016/j.apjo.2024.100087","url":null,"abstract":"<div><h3>Purpose</h3><p>Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visualising the important features responsible for predictions. This ultimately improves interpretability and confidence. In this work, we review the use case for SMs, exploring their impact on clinicians’ understanding and trust in AI models. We use the following ophthalmic conditions as examples: (1) glaucoma, (2) myopia, (3) age-related macular degeneration, and (4) diabetic retinopathy.</p></div><div><h3>Method</h3><p>A multi-field search on MEDLINE, Embase, and Web of Science was conducted using specific keywords. Only studies on the use of SMs in glaucoma, myopia, AMD, or DR were considered for inclusion.</p></div><div><h3>Results</h3><p>Findings reveal that SMs are often used to validate AI models and advocate for their adoption, potentially leading to biased claims. Overlooking the technical limitations of SMs, and the conductance of superficial assessments of their quality and relevance, was discerned. Uncertainties persist regarding the role of saliency maps in building trust in AI. It is crucial to enhance understanding of SMs' technical constraints and improve evaluation of their quality, impact, and suitability for specific tasks. Establishing a standardised framework for selecting and assessing SMs, as well as exploring their relationship with other reliability sources (e.g. safety and generalisability), is essential for enhancing clinicians' trust in AI.</p></div><div><h3>Conclusion</h3><p>We conclude that SMs are not beneficial for interpretability and trust-building purposes in their current forms. Instead, SMs may confer benefits to model debugging, model performance enhancement, and hypothesis testing (e.g. novel biomarkers).</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000884/pdfft?md5=947c519db2eaf73afca2cbfe4639495c&pid=1-s2.0-S2162098924000884-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100084
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records –one of its most well-known and frequently exploited uses– to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.
{"title":"Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician","authors":"","doi":"10.1016/j.apjo.2024.100084","DOIUrl":"10.1016/j.apjo.2024.100084","url":null,"abstract":"<div><p>Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records –one of its most well-known and frequently exploited uses– to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000859/pdfft?md5=4d1793e4d147d08d7a1dbc7d3ffdca4e&pid=1-s2.0-S2162098924000859-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141765063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100080
{"title":"Diversity, equity and inclusion in curriculum vitae for medical and surgical specialty training college entrance","authors":"","doi":"10.1016/j.apjo.2024.100080","DOIUrl":"10.1016/j.apjo.2024.100080","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000811/pdfft?md5=6077f5383344802042eca893085dc17b&pid=1-s2.0-S2162098924000811-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100090
The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.
{"title":"Latest developments of generative artificial intelligence and applications in ophthalmology","authors":"","doi":"10.1016/j.apjo.2024.100090","DOIUrl":"10.1016/j.apjo.2024.100090","url":null,"abstract":"<div><p>The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000914/pdfft?md5=d7ea0e26c3fa992ee3c0a0c2b8d57bdc&pid=1-s2.0-S2162098924000914-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100092
{"title":"Longitudinal imaging of 8-year progression in a teenager with Stargardt disease","authors":"","doi":"10.1016/j.apjo.2024.100092","DOIUrl":"10.1016/j.apjo.2024.100092","url":null,"abstract":"","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000938/pdfft?md5=9f8432b7530bdc6f222d91df2993bd83&pid=1-s2.0-S2162098924000938-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141981508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1016/j.apjo.2024.100085
Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mimic natural language comprehension and generation. Their evolution has undergone several waves of innovation similar to convolutional neural networks. The transformer architecture advancement in generative artificial intelligence marks a monumental leap beyond early-stage pattern recognition via supervised learning. With the expansion of parameters and training data (terabytes), LLMs unveil remarkable human interactivity, encompassing capabilities such as memory retention and comprehension. These advances make LLMs particularly well-suited for roles in healthcare communication between medical practitioners and patients. In this comprehensive review, we discuss the trajectory of LLMs and their potential implications for clinicians and patients. For clinicians, LLMs can be used for automated medical documentation, and given better inputs and extensive validation, LLMs may be able to autonomously diagnose and treat in the future. For patient care, LLMs can be used for triage suggestions, summarization of medical documents, explanation of a patient’s condition, and customizing patient education materials tailored to their comprehension level. The limitations of LLMs and possible solutions for real-world use are also presented. Given the rapid advancements in this area, this review attempts to briefly cover many roles that LLMs may play in the ophthalmic space, with a focus on improving the quality of healthcare delivery.
{"title":"Understanding natural language: Potential application of large language models to ophthalmology","authors":"","doi":"10.1016/j.apjo.2024.100085","DOIUrl":"10.1016/j.apjo.2024.100085","url":null,"abstract":"<div><p>Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mimic natural language comprehension and generation. Their evolution has undergone several waves of innovation similar to convolutional neural networks. The transformer architecture advancement in generative artificial intelligence marks a monumental leap beyond early-stage pattern recognition via supervised learning. With the expansion of parameters and training data (terabytes), LLMs unveil remarkable human interactivity, encompassing capabilities such as memory retention and comprehension. These advances make LLMs particularly well-suited for roles in healthcare communication between medical practitioners and patients. In this comprehensive review, we discuss the trajectory of LLMs and their potential implications for clinicians and patients. For clinicians, LLMs can be used for automated medical documentation, and given better inputs and extensive validation, LLMs may be able to autonomously diagnose and treat in the future. For patient care, LLMs can be used for triage suggestions, summarization of medical documents, explanation of a patient’s condition, and customizing patient education materials tailored to their comprehension level. The limitations of LLMs and possible solutions for real-world use are also presented. Given the rapid advancements in this area, this review attempts to briefly cover many roles that LLMs may play in the ophthalmic space, with a focus on improving the quality of healthcare delivery.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000860/pdfft?md5=a575f29dafd0fc0b04c2b745059b621b&pid=1-s2.0-S2162098924000860-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141765064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}