{"paper_id":"11fde114-c01f-404e-a5c2-dfb2a7c2422e","body_text":"AI Performance on Image-based Medical Case Scenarios: A Cross-Sectional Comparative Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI Performance on Image-based Medical Case Scenarios: A Cross-Sectional Comparative Study Jia-Wei Liu, Yue-Tong Qian, Xiao Ma, Jun-Ping Fan, Lan-Wei Guo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8096665/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Large language models (LLMs) have shown remarkable progress in text-based tasks, but their ability to interpret and respond to image-based clinical scenarios remains underexplored. This study evaluated and compared the performance of ChatGPT-5 and Claude in answering subjective image-based medical case questions. Methods A cross-sectional comparative study was conducted using 71 dermatological case scenarios subjective questions designed by the research team. Each AI system generated responses to identical visual and textual inputs without external assistance. Two experienced dermatologists, blinded to model identity, independently scored the responses against standard answers. Inter-rater reliability was assessed using intraclass correlation coefficients (ICC), and comparative analyses employed Mann–Whitney U tests, Bland–Altman plots, and correlation metrics. Results Both evaluators demonstrated excellent inter-rater reliability (ICC > 0.86). Claude achieved higher mean scores (27.39 ± 11.44) than ChatGPT-5 (25.53 ± 11.45; p < 0.001). Claude also showed stronger correlation with reference standards (ρ = 0.88 vs. 0.83), lower mean absolute error (14.76% vs. 19.98%), and reduced root mean square error (7.24 vs. 9.24). Bland–Altman analysis revealed minimal systematic bias between evaluators, indicating consistent scoring reliability. Conclusions Both multimodal LLMs demonstrated strong competence in interpreting image-based medical scenarios. Claude exhibited a modest but consistent advantage in diagnostic reasoning and clinical alignment. These findings support the potential of LLMs as supplementary educational tools in visual disciplines such as dermatology, emphasizing the importance of model selection, supervised use, and continued evaluation as AI integration in medical education expands. large language model artificial intelligence multimodal AI medical education image-based assessment Full Text Additional Declarations No competing interests reported. Supplementary Files supplementarySubjectiveQuestionsAssessmentScoringTable.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Editor invited by journal 08 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 08 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8096665\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":558120981,\"identity\":\"e4c5c66b-d7c7-4448-a7b4-ad853dc1d772\",\"order_by\":0,\"name\":\"Jia-Wei Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical 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Education](http://bmcmededuc.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/meed/default.aspx\",\"title\":\"BMC Medical Education\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"large language model, artificial intelligence, multimodal AI, medical education, image-based assessment\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8096665/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8096665/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eLarge language models (LLMs) have shown remarkable progress in text-based tasks, but their ability to interpret and respond to image-based clinical scenarios remains underexplored. This study evaluated and compared the performance of ChatGPT-5 and Claude in answering subjective image-based medical case questions.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eA cross-sectional comparative study was conducted using 71 dermatological case scenarios subjective questions designed by the research team. Each AI system generated responses to identical visual and textual inputs without external assistance. Two experienced dermatologists, blinded to model identity, independently scored the responses against standard answers. Inter-rater reliability was assessed using intraclass correlation coefficients (ICC), and comparative analyses employed Mann\\u0026ndash;Whitney U tests, Bland\\u0026ndash;Altman plots, and correlation metrics.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eBoth evaluators demonstrated excellent inter-rater reliability (ICC\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.86). Claude achieved higher mean scores (27.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.44) than ChatGPT-5 (25.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.45; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Claude also showed stronger correlation with reference standards (ρ\\u0026thinsp;=\\u0026thinsp;0.88 vs. 0.83), lower mean absolute error (14.76% vs. 19.98%), and reduced root mean square error (7.24 vs. 9.24). Bland\\u0026ndash;Altman analysis revealed minimal systematic bias between evaluators, indicating consistent scoring reliability.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e\\u003cp\\u003eBoth multimodal LLMs demonstrated strong competence in interpreting image-based medical scenarios. Claude exhibited a modest but consistent advantage in diagnostic reasoning and clinical alignment. These findings support the potential of LLMs as supplementary educational tools in visual disciplines such as dermatology, emphasizing the importance of model selection, supervised use, and continued evaluation as AI integration in medical education expands.\\u003c/p\\u003e\",\"manuscriptTitle\":\"AI Performance on Image-based Medical Case Scenarios: A Cross-Sectional Comparative Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-13 01:17:22\",\"doi\":\"10.21203/rs.3.rs-8096665/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T08:28:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"213370811993672064481995833645905798445\",\"date\":\"2026-04-29T15:28:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-12-09T09:32:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-12-09T09:26:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-12-09T04:56:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-12-08T14:48:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Medical Education\",\"date\":\"2025-12-08T14:34:11+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medical-education\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"meed\",\"sideBox\":\"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/meed/default.aspx\",\"title\":\"BMC Medical Education\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"6471fdf3-8bf2-4457-90fe-d0f315e555d3\",\"owner\":[],\"postedDate\":\"December 13th, 2025\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T08:28:56+00:00\",\"index\":125,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-13T01:17:22+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-13 01:17:22\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8096665\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8096665\",\"identity\":\"rs-8096665\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}