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      Comparative performance of humans versus GPT-4.0 and GPT-3.5 in the self-assessment program of American Academy of Ophthalmology

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          Abstract

          To compare the performance of humans, GPT-4.0 and GPT-3.5 in answering multiple-choice questions from the American Academy of Ophthalmology (AAO) Basic and Clinical Science Course (BCSC) self-assessment program, available at https://www.aao.org/education/self-assessments. In June 2023, text-based multiple-choice questions were submitted to GPT-4.0 and GPT-3.5. The AAO provides the percentage of humans who selected the correct answer, which was analyzed for comparison. All questions were classified by 10 subspecialties and 3 practice areas (diagnostics/clinics, medical treatment, surgery). Out of 1023 questions, GPT-4.0 achieved the best score (82.4%), followed by humans (75.7%) and GPT-3.5 (65.9%), with significant difference in accuracy rates (always P < 0.0001). Both GPT-4.0 and GPT-3.5 showed the worst results in surgery-related questions (74.6% and 57.0% respectively). For difficult questions (answered incorrectly by > 50% of humans), both GPT models favorably compared to humans, without reaching significancy. The word count for answers provided by GPT-4.0 was significantly lower than those produced by GPT-3.5 (160 ± 56 and 206 ± 77 respectively, P < 0.0001); however, incorrect responses were longer (P < 0.02). GPT-4.0 represented a substantial improvement over GPT-3.5, achieving better performance than humans in an AAO BCSC self-assessment test. However, ChatGPT is still limited by inconsistency across different practice areas, especially when it comes to surgery.

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          ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

          ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.
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            How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

            Background Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results Of the 4 data sets, AMBOSS-Step1 , AMBOSS-Step2 , NBME-Free-Step1 , and NBME-Free-Step2 , ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased ( P =.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 ( P <.001) and NBME-Free-Step2 ( P =.001) data sets, respectively. Conclusions ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT’s capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.
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              Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

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                Author and article information

                Contributors
                giuseppe.giannaccare@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 October 2023
                29 October 2023
                2023
                : 13
                : 18562
                Affiliations
                [1 ]GRID grid.411489.1, ISNI 0000 0001 2168 2547, Department of Ophthalmology, , University Magna Graecia of Catanzaro, ; Catanzaro, Italy
                [2 ]Department of Clinical Sciences and Translational Medicine, University of Rome Tor Vergata, ( https://ror.org/02p77k626) Rome, Italy
                [3 ]Department of Surgical Sciences, Eye Clinic, University of Cagliari, ( https://ror.org/003109y17) Via Università 40, 09124 Cagliari, Italy
                Author information
                http://orcid.org/0000-0003-2617-0289
                Article
                45837
                10.1038/s41598-023-45837-2
                10613606
                37899405
                a89f852d-22af-44b0-b81c-f4ca9eeade06
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 July 2023
                : 24 October 2023
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                © Springer Nature Limited 2023

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                health care,medical research
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                health care, medical research

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