The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Report: A Multicenter Study

The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Report: A Multicenter 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 The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Report: A Multicenter Study Yujie Xie, Bing Zhan, Kangfan Zhang, Yuchen Li, Jiarui Liu, Chunping Ning This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7574125/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 Objective To assess two large language models (LLMs), DeepSeek-R1 and ChatGPT-4o, in interpreting thyroid nodule ultrasound text report, emphasizing the accuracy in benign-malignant differentiation, the agreement of Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) classification and management recommendation, and the stability of each task. Methods We analyzed 1,063 ultrasound text reports from three medical centers, with 306 nodules confirmed by histopathology. Each nodule's report was processed through two LLMs using standardized prompts, repeated five times, with the final result determined by mode voting. Results DeepSeek-R1 excelled over ChatGPT-4o in differentiating benign from malignant nodules, with superior sensitivity (0.879 vs. 0.692), accuracy (0.729 vs. 0.644), and Area Under the Curve (AUC) (0.694 vs. 0.632). However, senior radiologists achieved notably better results with higher accuracy (0.804), and AUC (0.865) compared two LLMs. In C-TIRADS classification, DeepSeek-R1 also outperformed ChatGPT-4o (κ = 0.770 vs. κ = 0.688, Δκ = 0.083 [95% CI: 0.048, 0.122]). Both models showed substantial agreement with clinicians on management recommendation (κ = 0.606 vs. κ = 0.608, Δκ=-0.002 [95% CI: -0.044, 0.041]). In terms of stability, LLMs exhibited almost perfect agreement in C-TIRADS classification (α = 0.864 vs. α = 0.866, Δα=-0.003 [95% CI: -0.023, 0.017]) and management recommendation (κ = 0.853 vs. κ = 0.849, Δκ = 0.004 [95% CI: -0.026, 0.033]). However, in benign-malignant discrimination, DeepSeek-R1 demonstrated significantly greater stability than ChatGPT-4o (κ = 0.849 vs. κ = 0.550, Δκ = 0.260 [95% CI: 0.191, 0.321]). Conclusion Our study highlights the potential of LLMs for interpreting thyroid nodule ultrasound text reports. DeepSeek-R1 outperformed in benign-malignant differentiation accuracy and classification consistency, whereas ChatGPT-4o and DeepSeek-R1 performed similarly in management recommendation. Large language models Thyroid nodule Ultrasound Performance Full Text Additional Declarations No competing interests reported. Supplementary Files 6SupplementalMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 14 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 09 Sep, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7574125","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531929289,"identity":"8dd3c17f-8e27-4cf6-a869-86b53133b5f7","order_by":0,"name":"Yujie Xie","email":"","orcid":"","institution":"the Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Xie","suffix":""},{"id":531929290,"identity":"599298d0-e1b1-4ce9-bb5a-1b72393cefad","order_by":1,"name":"Bing Zhan","email":"","orcid":"","institution":"JiaoZhou Central Hospital of 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Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Large language models, Thyroid nodule, Ultrasound, Performance","lastPublishedDoi":"10.21203/rs.3.rs-7574125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7574125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo assess two large language models (LLMs), DeepSeek-R1 and ChatGPT-4o, in interpreting thyroid nodule ultrasound text report, emphasizing the accuracy in benign-malignant differentiation, the agreement of Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) classification and management recommendation, and the stability of each task.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed 1,063 ultrasound text reports from three medical centers, with 306 nodules confirmed by histopathology. Each nodule's report was processed through two LLMs using standardized prompts, repeated five times, with the final result determined by mode voting.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDeepSeek-R1 excelled over ChatGPT-4o in differentiating benign from malignant nodules, with superior sensitivity (0.879 vs. 0.692), accuracy (0.729 vs. 0.644), and Area Under the Curve (AUC) (0.694 vs. 0.632). However, senior radiologists achieved notably better results with higher accuracy (0.804), and AUC (0.865) compared two LLMs. In C-TIRADS classification, DeepSeek-R1 also outperformed ChatGPT-4o (κ\u0026thinsp;=\u0026thinsp;0.770 vs. κ\u0026thinsp;=\u0026thinsp;0.688, Δκ\u0026thinsp;=\u0026thinsp;0.083 [95% CI: 0.048, 0.122]). Both models showed substantial agreement with clinicians on management recommendation (κ\u0026thinsp;=\u0026thinsp;0.606 vs. κ\u0026thinsp;=\u0026thinsp;0.608, Δκ=-0.002 [95% CI: -0.044, 0.041]). In terms of stability, LLMs exhibited almost perfect agreement in C-TIRADS classification (α\u0026thinsp;=\u0026thinsp;0.864 vs. α\u0026thinsp;=\u0026thinsp;0.866, Δα=-0.003 [95% CI: -0.023, 0.017]) and management recommendation (κ\u0026thinsp;=\u0026thinsp;0.853 vs. κ\u0026thinsp;=\u0026thinsp;0.849, Δκ\u0026thinsp;=\u0026thinsp;0.004 [95% CI: -0.026, 0.033]). However, in benign-malignant discrimination, DeepSeek-R1 demonstrated significantly greater stability than ChatGPT-4o (κ\u0026thinsp;=\u0026thinsp;0.849 vs. κ\u0026thinsp;=\u0026thinsp;0.550, Δκ\u0026thinsp;=\u0026thinsp;0.260 [95% CI: 0.191, 0.321]).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur study highlights the potential of LLMs for interpreting thyroid nodule ultrasound text reports. DeepSeek-R1 outperformed in benign-malignant differentiation accuracy and classification consistency, whereas ChatGPT-4o and DeepSeek-R1 performed similarly in management recommendation.\u003c/p\u003e","manuscriptTitle":"The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Report: A Multicenter Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 17:57:29","doi":"10.21203/rs.3.rs-7574125/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-04T21:42:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120404124463081768260700223499091494793","date":"2025-10-13T06:43:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T17:24:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-14T16:36:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T07:37:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T07:36:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-09-09T12:54:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f55d160-4a4e-4d16-a776-2bfd9c573497","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T17:57:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 17:57:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7574125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7574125","identity":"rs-7574125","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}