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Munir WM, Munir SZ. Evaluation of Sociomedical Factors on Corneal Donor Recovery Using Machine Learning. Ophthalmic Epidemiol 2024:1-8. [PMID: 39288325 DOI: 10.1080/09286586.2024.2399350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/26/2024] [Accepted: 08/27/2024] [Indexed: 09/19/2024]
Abstract
PURPOSE To evaluate co-morbid sociomedical conditions affecting corneal donor endothelial cell density and transplant suitability. METHOD(S) Corneal donor transplant information was collected from the CorneaGen eye bank between June 1, 2012 and June 30, 2016. A natural language processing algorithm was applied to generate co-morbid sociomedical conditions for each donor. Variables of importance were identified using four machine learning models (random forest, Glmnet, Earth, nnet), for the outcomes of transplant suitability and endothelial cell density. SHAP (SHapley Additive exPlanations) values were generated, with beeswarm and box plots to visualize the contribution of each feature to the models. RESULTS With a total of 23,522 unique donors, natural language processing generated 30,573 indices, which were reduced to 41 most common co-morbid sociomedical conditions. For transplant suitability, hypertension ranked the top overall variable of importance in two models. Hypertension, chronic obstructive pulmonary disease, history of smoking, and alcohol use appeared consistently in the top variables of importance. By SHAP feature importance, hypertension (0.042), alcohol use (0.017), ventilation of donor (0.011), and history of smoking (0.010) contributed the most to the transplant suitability model. For endothelial cell density, hypertension was the sociomedical condition of highest importance in three models. SHAP scores were highest among the sociomedical conditions of hypertension (0.037), alcohol use (0.013), myocardial infarction (0.012), and history of smoking (0.011). CONCLUSION In a large cohort of corneal donor eyes, hypertension was identified as the most common contributor to machine learning models examining sociomedical conditions for corneal donor transplant suitability and endothelial cell density.
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Affiliation(s)
- Wuqaas M Munir
- Department of Ophthalmology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Saleha Z Munir
- Department of Ophthalmology, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Anguita R, Downie C, Ferro Desideri L, Sagoo MS. Assessing large language models' accuracy in providing patient support for choroidal melanoma. Eye (Lond) 2024:10.1038/s41433-024-03231-w. [PMID: 39003430 DOI: 10.1038/s41433-024-03231-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/17/2024] [Accepted: 07/09/2024] [Indexed: 07/15/2024] Open
Abstract
PURPOSE This study aimed to evaluate the accuracy of information that patients can obtain from large language models (LLMs) when seeking answers to common questions about choroidal melanoma. METHODS Comparative study comparing frequently asked questions from choroidal melanoma patients and queried three major LLMs-ChatGPT 3.5, Bing AI, and DocsGPT. Answers were reviewed by three ocular oncology experts and scored as accurate, partially accurate, or inaccurate. Statistical analysis compared the quality of responses across models. RESULTS For medical advice questions, ChatGPT gave 92% accurate responses compared to 58% for Bing AI and DocsGPT. For pre/post-op questions, ChatGPT and Bing AI were 86% accurate while DocsGPT was 73% accurate. There were no statistically significant differences between models. ChatGPT responses were the longest while Bing AI responses were the shortest, but length did not affect accuracy. All LLMs appropriately directed patients to seek medical advice from professionals. CONCLUSION LLMs show promising capability to address common choroidal melanoma patient questions at generally acceptable accuracy levels. However, inconsistent, and inaccurate responses do occur, highlighting the need for improved fine-tuning and oversight before integration into clinical practice.
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Affiliation(s)
- Rodrigo Anguita
- Moorfields Eye Hospital NHS Foundation Trust, City Road London, London, UK.
- Department of Ophthalmology, Inselspital University Hospital of Bern, Bern, Switzerland.
| | - Catriona Downie
- Moorfields Eye Hospital NHS Foundation Trust, City Road London, London, UK
| | | | - Mandeep S Sagoo
- Moorfields Eye Hospital NHS Foundation Trust, City Road London, London, UK
- NIHR Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, UK
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Kedia N, Sanjeev S, Ong J, Chhablani J. ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology. Eye (Lond) 2024; 38:1252-1261. [PMID: 38172581 PMCID: PMC11076576 DOI: 10.1038/s41433-023-02915-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 11/23/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
ChatGPT, an artificial intelligence (AI) chatbot built on large language models (LLMs), has rapidly gained popularity. The benefits and limitations of this transformative technology have been discussed across various fields, including medicine. The widespread availability of ChatGPT has enabled clinicians to study how these tools could be used for a variety of tasks such as generating differential diagnosis lists, organizing patient notes, and synthesizing literature for scientific research. LLMs have shown promising capabilities in ophthalmology by performing well on the Ophthalmic Knowledge Assessment Program, providing fairly accurate responses to questions about retinal diseases, and in generating differential diagnoses list. There are current limitations to this technology, including the propensity of LLMs to "hallucinate", or confidently generate false information; their potential role in perpetuating biases in medicine; and the challenges in incorporating LLMs into research without allowing "AI-plagiarism" or publication of false information. In this paper, we provide a balanced overview of what LLMs are and introduce some of the LLMs that have been generated in the past few years. We discuss recent literature evaluating the role of these language models in medicine with a focus on ChatGPT. The field of AI is fast-paced, and new applications based on LLMs are being generated rapidly; therefore, it is important for ophthalmologists to be aware of how this technology works and how it may impact patient care. Here, we discuss the benefits, limitations, and future advancements of LLMs in patient care and research.
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Affiliation(s)
- Nikita Kedia
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, USA
| | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Biswas S, Davies LN, Sheppard AL, Logan NS, Wolffsohn JS. Utility of artificial intelligence-based large language models in ophthalmic care. Ophthalmic Physiol Opt 2024; 44:641-671. [PMID: 38404172 DOI: 10.1111/opo.13284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 02/27/2024]
Abstract
PURPOSE With the introduction of ChatGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human-like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under-reported. RECENT FINDINGS Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question-based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI-based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible-sounding 'fake' responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI-based LLMs. SUMMARY Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.
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Affiliation(s)
- Sayantan Biswas
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Leon N Davies
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Amy L Sheppard
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Nicola S Logan
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - James S Wolffsohn
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
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Lakkimsetti M, Devella SG, Patel KB, Dhandibhotla S, Kaur J, Mathew M, Kataria J, Nallani M, Farwa UE, Patel T, Egbujo UC, Meenashi Sundaram D, Kenawy S, Roy M, Khan SF. Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature. Cureus 2024; 16:e58400. [PMID: 38756258 PMCID: PMC11098056 DOI: 10.7759/cureus.58400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.
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Affiliation(s)
| | - Swati G Devella
- Medicine, Kempegowda Institute of Medical Sciences, Bangalore, IND
| | - Keval B Patel
- Surgery, Narendra Modi Medical College, Ahmedabad, IND
| | | | | | - Midhun Mathew
- Internal Medicine, Trinitas Regional Medical Center, Elizabeth, USA
| | | | - Manisha Nallani
- Medicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND
| | - Umm E Farwa
- Emergency Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Tirath Patel
- Medicine, American University of Antigua, Saint John's, ATG
| | | | - Dakshin Meenashi Sundaram
- Internal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND
| | | | - Mehak Roy
- Internal Medicine, School of Medicine Science and Research, Delhi, IND
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Ittarat M, Cheungpasitporn W, Chansangpetch S. Personalized Care in Eye Health: Exploring Opportunities, Challenges, and the Road Ahead for Chatbots. J Pers Med 2023; 13:1679. [PMID: 38138906 PMCID: PMC10744965 DOI: 10.3390/jpm13121679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
In modern eye care, the adoption of ophthalmology chatbots stands out as a pivotal technological progression. These digital assistants present numerous benefits, such as better access to vital information, heightened patient interaction, and streamlined triaging. Recent evaluations have highlighted their performance in both the triage of ophthalmology conditions and ophthalmology knowledge assessment, underscoring their potential and areas for improvement. However, assimilating these chatbots into the prevailing healthcare infrastructures brings challenges. These encompass ethical dilemmas, legal compliance, seamless integration with electronic health records (EHR), and fostering effective dialogue with medical professionals. Addressing these challenges necessitates the creation of bespoke standards and protocols for ophthalmology chatbots. The horizon for these chatbots is illuminated by advancements and anticipated innovations, poised to redefine the delivery of eye care. The synergy of artificial intelligence (AI) and machine learning (ML) with chatbots amplifies their diagnostic prowess. Additionally, their capability to adapt linguistically and culturally ensures they can cater to a global patient demographic. In this article, we explore in detail the utilization of chatbots in ophthalmology, examining their accuracy, reliability, data protection, security, transparency, potential algorithmic biases, and ethical considerations. We provide a comprehensive review of their roles in the triage of ophthalmology conditions and knowledge assessment, emphasizing their significance and future potential in the field.
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Affiliation(s)
- Mantapond Ittarat
- Surin Hospital and Surin Medical Education Center, Suranaree University of Technology, Surin 32000, Thailand;
| | | | - Sunee Chansangpetch
- Center of Excellence in Glaucoma, Chulalongkorn University, Bangkok 10330, Thailand;
- Department of Ophthalmology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok 10330, Thailand
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Ferro Desideri L, Roth J, Zinkernagel M, Anguita R. "Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration". Int J Retina Vitreous 2023; 9:71. [PMID: 37980501 PMCID: PMC10657493 DOI: 10.1186/s40942-023-00511-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023] Open
Abstract
INTRODUCTION Age-related macular degeneration (AMD) affects millions of people globally, leading to a surge in online research of putative diagnoses, causing potential misinformation and anxiety in patients and their parents. This study explores the efficacy of artificial intelligence-derived large language models (LLMs) like in addressing AMD patients' questions. METHODS ChatGPT 3.5 (2023), Bing AI (2023), and Google Bard (2023) were adopted as LLMs. Patients' questions were subdivided in two question categories, (a) general medical advice and (b) pre- and post-intravitreal injection advice and classified as (1) accurate and sufficient (2) partially accurate but sufficient and (3) inaccurate and not sufficient. Non-parametric test has been done to compare the means between the 3 LLMs scores and also an analysis of variance and reliability tests were performed among the 3 groups. RESULTS In category a) of questions, the average score was 1.20 (± 0.41) with ChatGPT 3.5, 1.60 (± 0.63) with Bing AI and 1.60 (± 0.73) with Google Bard, showing no significant differences among the 3 groups (p = 0.129). The average score in category b was 1.07 (± 0.27) with ChatGPT 3.5, 1.69 (± 0.63) with Bing AI and 1.38 (± 0.63) with Google Bard, showing a significant difference among the 3 groups (p = 0.0042). Reliability statistics showed Chronbach's α of 0.237 (range 0.448, 0.096-0.544). CONCLUSION ChatGPT 3.5 consistently offered the most accurate and satisfactory responses, particularly with technical queries. While LLMs displayed promise in providing precise information about AMD; however, further improvements are needed especially in more technical questions.
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Affiliation(s)
- Lorenzo Ferro Desideri
- Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, Switzerland.
- Bern Photographic Reading Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Janice Roth
- Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, Switzerland
| | - Martin Zinkernagel
- Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, Switzerland
- Bern Photographic Reading Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Rodrigo Anguita
- Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, Switzerland
- Moorfields Eye Hospital NHS Foundation Trust, City Road, London, EC1V 2PD, UK
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Chakraborty C, Pal S, Bhattacharya M, Dash S, Lee SS. Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science. Front Artif Intell 2023; 6:1237704. [PMID: 38028668 PMCID: PMC10644239 DOI: 10.3389/frai.2023.1237704] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, India
| | - Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | | | - Snehasish Dash
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon-si, Gangwon-do, Republic of Korea
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