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      Evaluating the strengths and limitations of multimodal ChatGPT-4 in detecting glaucoma using fundus images

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          Abstract

          Overview

          This study evaluates the diagnostic accuracy of a multimodal large language model (LLM), ChatGPT-4, in recognizing glaucoma using color fundus photographs (CFPs) with a benchmark dataset and without prior training or fine tuning.

          Methods

          The publicly accessible Retinal Fundus Glaucoma Challenge “REFUGE” dataset was utilized for analyses. The input data consisted of the entire 400 image testing set. The task involved classifying fundus images into either ‘Likely Glaucomatous’ or ‘Likely Non-Glaucomatous’. We constructed a confusion matrix to visualize the results of predictions from ChatGPT-4, focusing on accuracy of binary classifications (glaucoma vs non-glaucoma).

          Results

          ChatGPT-4 demonstrated an accuracy of 90% with a 95% confidence interval (CI) of 87.06%-92.94%. The sensitivity was found to be 50% (95% CI: 34.51%-65.49%), while the specificity was 94.44% (95% CI: 92.08%-96.81%). The precision was recorded at 50% (95% CI: 34.51%-65.49%), and the F1 Score was 0.50.

          Conclusion

          ChatGPT-4 achieved relatively high diagnostic accuracy without prior fine tuning on CFPs. Considering the scarcity of data in specialized medical fields, including ophthalmology, the use of advanced AI techniques, such as LLMs, might require less data for training compared to other forms of AI with potential savings in time and financial resources. It may also pave the way for the development of innovative tools to support specialized medical care, particularly those dependent on multimodal data for diagnosis and follow-up, irrespective of resource constraints.

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          Most cited references18

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          Artificial Hallucinations in ChatGPT: Implications in Scientific Writing

          While still in its infancy, ChatGPT (Generative Pretrained Transformer), introduced in November 2022, is bound to hugely impact many industries, including healthcare, medical education, biomedical research, and scientific writing. Implications of ChatGPT, that new chatbot introduced by OpenAI on academic writing, is largely unknown. In response to the Journal of Medical Science (Cureus) Turing Test - call for case reports written with the assistance of ChatGPT, we present two cases one of homocystinuria-associated osteoporosis, and the other is on late-onset Pompe disease (LOPD), a rare metabolic disorder. We tested ChatGPT to write about the pathogenesis of these conditions. We documented the positive, negative, and rather troubling aspects of our newly introduced chatbot’s performance.
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            History of artificial intelligence in medicine

            Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now that AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which are reviewed in further detail by several other articles in this issue of Gastrointestinal Endoscopy.
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              REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/661648Role: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2603383Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role:
                Journal
                Front Ophthalmol (Lausanne)
                Front Ophthalmol (Lausanne)
                Front. Ophthalmol.
                Frontiers in Ophthalmology
                Frontiers Media S.A.
                2674-0826
                07 June 2024
                2024
                : 4
                : 1387190
                Affiliations
                [1] 1 Department of Ophthalmology, The University of Jordan , Amman, Jordan
                [2] 2 Department of Ophthalmology, Houston Methodist Hospital , Houston, TX, United States
                [3] 3 Jordan University Hospital , Amman, Jordan
                [4] 4 Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center , Aurora, CO, United States
                Author notes

                Edited by: Jean-Claude Mwanza, University of North Carolina at Chapel Hill, United States

                Reviewed by: Gilbert Yong San Lim, SingHealth, Singapore

                Michael Balas, University of Toronto, Canada

                *Correspondence: Ayman Mohammed Musleh, aimanmesleh@ 123456gmail.com
                Article
                10.3389/fopht.2024.1387190
                11182172
                38984105
                7a985359-f1e1-4187-be69-a1f19aeb1ebd
                Copyright © 2024 AlRyalat, Musleh and Kahook

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 16 February 2024
                : 17 May 2024
                Page count
                Figures: 2, Tables: 4, Equations: 5, References: 18, Pages: 6, Words: 2943
                Funding
                The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
                Categories
                Ophthalmology
                Original Research
                Custom metadata
                Glaucoma

                large language models,glaucoma,artificial intelligence,chatgpt,gpt

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