{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T16:42:55Z","timestamp":1789836175303,"version":"4.0.1"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100017689","name":"Guangdong Provincial People's Hospital","doi-asserted-by":"publisher","award":["BY01202266"],"award-info":[{"award-number":["BY01202266"]}],"id":[{"id":"10.13039\/501100017689","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction\u2010associated steatotic liver disease (MASLD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We included individuals from NHANES 2017\u20132018. The validity and reliability of MASLD diagnosis by GPT\u20103.5 and GPT\u20104 were quantitatively examined and compared with those of the Fatty Liver Index (FLI) and United States FLI (USFLI). A receiver operating characteristic curve was conducted to assess the accuracy of MASLD diagnosis via different scoring systems. Additionally, GPT\u20104V's potential in clinical diagnosis using ultrasound images from MASLD patients was evaluated to provide assessments of LLM capabilities in both textual and visual data interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      GPT\u20104 demonstrated comparable performance in MASLD diagnosis to FLI and USFLI with the AUROC values of .831 (95% CI .796\u2013.867), .817 (95% CI .797\u2013.837) and .827 (95% CI .807\u2013.848), respectively. GPT\u20104 exhibited a trend of enhanced accuracy, clinical relevance and efficiency compared to GPT\u20103.5 based on clinician evaluation. Additionally, Pearson's\n                      <jats:italic>r<\/jats:italic>\n                      values between GPT\u20104 and FLI, as well as USFLI, were .718 and .695, respectively, indicating robust and moderate correlations. Moreover, GPT\u20104V showed potential in understanding characteristics from hepatic ultrasound imaging but exhibited limited interpretive accuracy in diagnosing MASLD compared to skilled radiologists.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>GPT\u20104 achieved performance comparable to traditional risk scores in diagnosing MASLD and exhibited improved convenience, versatility and the capacity to offer user\u2010friendly outputs. The integration of GPT\u20104V highlights the capacities of LLMs in handling both textual and visual medical data, reinforcing their expansive utility in healthcare practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.16112","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T06:28:46Z","timestamp":1731306526000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Exploring the potential of large language models in identifying metabolic dysfunction\u2010associated steatotic liver disease: A comparative study of non\u2010invasive tests and artificial intelligence\u2010generated responses"],"prefix":"10.1111","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6606-3840","authenticated-orcid":false,"given":"Wanying","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Cardiology Guangdong Cardiovascular Institute, Guangdong Provincial 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