William Rojas-Carabali , Rajdeep Agrawal , Laura Gutierrez-Sinisterra , Sally L. Baxter , Carlos Cifuentes-González , Yap Chun Wei , John Abisheganaden , Palvannan Kannapiran , Sunny Wong , Bernett Lee , Alejandra de-la-Torre , Rupesh Agrawal
{"title":"Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician","authors":"William Rojas-Carabali , Rajdeep Agrawal , Laura Gutierrez-Sinisterra , Sally L. Baxter , Carlos Cifuentes-González , Yap Chun Wei , John Abisheganaden , Palvannan Kannapiran , Sunny Wong , Bernett Lee , Alejandra de-la-Torre , Rupesh Agrawal","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":"13 4","pages":"Article 100084"},"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}
Thomas Muecke , Eiman Usmani , Stephen Bacchi, Robert J. Casson, Weng Onn Chan
{"title":"Diversity, equity and inclusion in curriculum vitae for medical and surgical specialty training college entrance","authors":"Thomas Muecke , Eiman Usmani , Stephen Bacchi, Robert J. Casson, Weng Onn Chan","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":"13 4","pages":"Article 100080"},"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}
Xiaoru Feng , Kezheng Xu , Ming-Jie Luo , Haichao Chen , Yangfan Yang , Qi He , Chenxin Song , Ruiyao Li , You Wu , Haibo Wang , Yih Chung Tham , Daniel Shu Wei Ting , Haotian Lin , Tien Yin Wong , Dennis Shun-chiu Lam
{"title":"Latest developments of generative artificial intelligence and applications in ophthalmology","authors":"Xiaoru Feng , Kezheng Xu , Ming-Jie Luo , Haichao Chen , Yangfan Yang , Qi He , Chenxin Song , Ruiyao Li , You Wu , Haibo Wang , Yih Chung Tham , Daniel Shu Wei Ting , Haotian Lin , Tien Yin Wong , Dennis Shun-chiu Lam","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":"13 4","pages":"Article 100090"},"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}
Zefeng Yang , Deming Wang , Fengqi Zhou , Diping Song , Yinhang Zhang , Jiaxuan Jiang , Kangjie Kong , Xiaoyi Liu , Yu Qiao , Robert T. Chang , Ying Han , Fei Li , Clement C. Tham , Xiulan Zhang
{"title":"Understanding natural language: Potential application of large language models to ophthalmology","authors":"Zefeng Yang , Deming Wang , Fengqi Zhou , Diping Song , Yinhang Zhang , Jiaxuan Jiang , Kangjie Kong , Xiaoyi Liu , Yu Qiao , Robert T. Chang , Ying Han , Fei Li , Clement C. Tham , Xiulan Zhang","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":"13 4","pages":"Article 100085"},"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}
Jennifer I. Lim , Aleksandra V. Rachitskaya , Joelle A. Hallak , Sina Gholami , Minhaj N. Alam
{"title":"Artificial intelligence for retinal diseases","authors":"Jennifer I. Lim , Aleksandra V. Rachitskaya , Joelle A. Hallak , Sina Gholami , Minhaj N. Alam","doi":"10.1016/j.apjo.2024.100096","DOIUrl":"10.1016/j.apjo.2024.100096","url":null,"abstract":"<div><h3>Purpose</h3><p>To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases.</p></div><div><h3>Methods</h3><p>We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search terms included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review articles.</p></div><div><h3>Results</h3><p>Research studies have investigated and shown the utility of AI for screening for diseases such as DR, AMD, ROP, and SCR. Research studies using validated and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research articles suggest AI may be useful for planning and performing robotic surgery. Studies suggest AI holds the potential to help lessen the impact of socioeconomic disparities on the outcomes of retinal diseases.</p></div><div><h3>Conclusions</h3><p>AI applications for retinal diseases can assist the clinician, not only by disease screening and monitoring for disease recurrence but also in quantitative analysis of treatment outcomes and prediction of treatment response. The public health impact on the prevention of blindness from DR, AMD, and other retinal vascular diseases remains to be determined.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100096"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000975/pdfft?md5=1f24208fb0d65df31ac85585a290a3b6&pid=1-s2.0-S2162098924000975-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103868","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}
{"title":"Longitudinal imaging of 8-year progression in a teenager with Stargardt disease","authors":"Chong Chen, Yuchen Zhang, Tianwei Qian, Suqin Yu","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":"13 4","pages":"Article 100092"},"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}
Zhen Ling Teo , Chrystie Wan Ning Quek , Joy Le Yi Wong , Daniel Shu Wei Ting
{"title":"Cybersecurity in the generative artificial intelligence era","authors":"Zhen Ling Teo , Chrystie Wan Ning Quek , Joy Le Yi Wong , Daniel Shu Wei Ting","doi":"10.1016/j.apjo.2024.100091","DOIUrl":"10.1016/j.apjo.2024.100091","url":null,"abstract":"<div><p>Generative Artificial Intelligence (GenAI) are algorithms capable of generating original content. The ability of GenAI to learn and generate novel outputs alike human cognition has taken the world by storm and ushered in a new era. In this review, we explore the role of GenAI in healthcare, including clinical, operational, and research applications, and delve into the cybersecurity risks of this technology. We discuss risks such as data privacy risks, data poisoning attacks, the propagation of bias, and hallucinations. In this review, we recommend risk mitigation strategies to enhance cybersecurity in GenAI technologies and further explore the use of GenAI as a tool in itself to enhance cybersecurity across the various AI algorithms. GenAI is emerging as a pivotal catalyst across various industries including the healthcare domain. Comprehending the intricacies of this technology and its potential risks will be imperative for us to fully capitalise on the benefits that GenAI can bring.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100091"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000926/pdfft?md5=e6d130e1d7f34e0f4b1d729d7677d647&pid=1-s2.0-S2162098924000926-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103869","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}
Joshua Ong , Kuk Jin Jang , Seung Ju Baek , Dongyin Hu , Vivian Lin , Sooyong Jang , Alexandra Thaler , Nouran Sabbagh , Almiqdad Saeed , Minwook Kwon , Jin Hyun Kim , Seongjin Lee , Yong Seop Han , Mingmin Zhao , Oleg Sokolsky , Insup Lee , Lama A. Al-Aswad
{"title":"Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians","authors":"Joshua Ong , Kuk Jin Jang , Seung Ju Baek , Dongyin Hu , Vivian Lin , Sooyong Jang , Alexandra Thaler , Nouran Sabbagh , Almiqdad Saeed , Minwook Kwon , Jin Hyun Kim , Seongjin Lee , Yong Seop Han , Mingmin Zhao , Oleg Sokolsky , Insup Lee , Lama A. Al-Aswad","doi":"10.1016/j.apjo.2024.100095","DOIUrl":"10.1016/j.apjo.2024.100095","url":null,"abstract":"<div><p>Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening leading to enhance in early detection and improve healthcare quality, particularly in underserved areas. However, the widespread adoption of such AI-based technologies faces challenges primarily related to the trustworthiness of the system. We demonstrate the potential and considerations needed to develop trustworthy AI in oculomics through a pilot study for HbA1c assessment using an AI-based approach. We then discuss various challenges, considerations, and solutions that have been developed for powerful AI technologies in the past in healthcare and subsequently apply these considerations to the oculomics pilot study. Building upon the observations in the study we highlight the challenges and opportunities for advancing trustworthy AI in oculomics. Ultimately, oculomics presents as a powerful and emerging technology in ophthalmology and understanding how to optimize transparency prior to clinical adoption is of utmost importance.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100095"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000963/pdfft?md5=acb8dd43f5863a99daef807eebad2890&pid=1-s2.0-S2162098924000963-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103870","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}
William Rojas-Carabali , Carlos Cifuentes-González , Laura Gutierrez-Sinisterra , Lim Yuan Heng , Edmund Tsui , Sapna Gangaputra , Srinivas Sadda , Quan Dong Nguyen , John H. Kempen , Carlos E. Pavesio , Vishali Gupta , Rajiv Raman , Chunyan Miao , Bernett Lee , Alejandra de-la-Torre , Rupesh Agrawal
{"title":"Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives","authors":"William Rojas-Carabali , Carlos Cifuentes-González , Laura Gutierrez-Sinisterra , Lim Yuan Heng , Edmund Tsui , Sapna Gangaputra , Srinivas Sadda , Quan Dong Nguyen , John H. Kempen , Carlos E. Pavesio , Vishali Gupta , Rajiv Raman , Chunyan Miao , Bernett Lee , Alejandra de-la-Torre , Rupesh Agrawal","doi":"10.1016/j.apjo.2024.100082","DOIUrl":"10.1016/j.apjo.2024.100082","url":null,"abstract":"<div><p>The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to health services. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease etiologies, analyze multimodal imaging data, predict outcomes, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.</p></div>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":"13 4","pages":"Article 100082"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2162098924000835/pdfft?md5=6458b1f573cc4656f4f4f828f864a316&pid=1-s2.0-S2162098924000835-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141632508","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}