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Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug-Induced Liver Injury.

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Τίτλος: Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug-Induced Liver Injury.
Συγγραφείς: Fu H; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Du Y; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Zhao G; Department of Infectious Diseases, Ruijin North Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Xiao Y; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Song S; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Zang X; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Zhou T; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Zhuang Y; Department of Infectious Diseases, Wuxi Xinrui Hospital, Shanghai Jiao Tong University School of Medicine, Wuxi, China., Mo R; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Lai R; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Xie Q; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Πηγή: Liver international : official journal of the International Association for the Study of the Liver [Liver Int] 2026 Oct; Vol. 46 (10), pp. e70879.
Τύπος έκδοσης: Journal Article; Multicenter Study; Comparative Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 101160857 Publication Model: Print Cited Medium: Internet ISSN: 1478-3231 (Electronic) Linking ISSN: 14783223 NLM ISO Abbreviation: Liver Int Subsets: MEDLINE
Imprint Name(s): Publication: Malden, MA : Wiley-Blackwell
Original Publication: Oxford, UK : Blackwell Munksgaard, c2003-
Ιατρικοί όροι (MeSH): Chemical and Drug Induced Liver Injury*/diagnosis , Chemical and Drug Induced Liver Injury*/mortality , Large Language Models* , Gastroenterology*, Humans ; Prognosis ; Male ; Female ; Middle Aged ; ROC Curve ; Clinical Decision-Making
Περίληψη: Background & Aims: Drug-induced liver injury (DILI) would progress to chronicity or death. Large language models (LLMs) may enhance clinical decision-making, yet their utility relative to physicians in DILI remains unclear. Therefore, we evaluated their performance in predicting DILI outcomes.
Methods: We enrolled 943 DILI patients from three centers as internal and external cohorts. Based on 12-month follow-up, outcomes were classified as recovery, 6/12-month chronicity, and death. LLMs (Gemini-2.5 Pro, GPT-5.1, DeepSeek-3.2), hepatologists (Junior, middle, senior), and models (Hy's Law, nHy's Law, MELD Score) estimated probabilities of outcomes. LLM-Rules (VOTE, OR, AND) were applied to enhance stability. Model performance was assessed.
Results: For 6-month chronicity, the senior achieved highest AUROC (0.61) with an accuracy of 70%. Gemini-2.5 Pro and GPT-5.1 yielded AUROCs of 0.60 and 0.59, respectively, outperforming junior and middle hepatologists. Gemini-2.5 Pro demonstrated strongest agreement with senior (κ = 0.43). LLMs all exhibited lower accuracy and specificity than hepatologists. A similar result was observed in 12-month chronicity. For overall mortality, the senior achieved highest AUROC (0.87) with an accuracy of 83%. Gemini-2.5 Pro and GPT-5.1 achieved AUROCs of 0.86, outperforming junior hepatologist, Hy's Law, and nHy's Law. GPT-5.1 achieved strongest agreement with senior (κ = 0.25). LLM-Rules demonstrated stability for predicting outcomes across cohorts. OR and AND rules improved sensitivity and specificity, respectively.
Conclusions: GPT-5.1 and Gemini-2.5 Pro showed AUROCs approaching senior hepatologists for DILI outcomes with limited accuracy and specificity. LLM-Rules demonstrated stable performance across cohorts with improved sensitivity or specificity, supporting the potential of multi-LLM approaches as clinician-supervised complementary tools.
(© 2026 The Author(s). Liver International published by John Wiley & Sons Ltd.)
References: Perspect Biol Med. 1968 Autumn;12(1):135-61. (PMID: 4387099)
J Med Syst. 2025 May 15;49(1):64. (PMID: 40372503)
JHEP Rep. 2025 Sep 03;7(12):101579. (PMID: 41321931)
Clin Mol Hepatol. 2023 Jul;29(3):721-732. (PMID: 36946005)
Hepatology. 2010 Dec;52(6):2065-76. (PMID: 20949552)
J Hepatol. 2016 Sep;65(3):532-42. (PMID: 27184533)
Gastroenterology. 2019 Nov;157(5):1245-1252.e3. (PMID: 31302142)
Liver Int. 2019 Jan;39(1):31-41. (PMID: 30003672)
Hepatology. 2000 Apr;31(4):864-71. (PMID: 10733541)
Front Immunol. 2022 Apr 12;13:772368. (PMID: 35493499)
Liver Int. 2025 Apr;45(4):e16112. (PMID: 39526465)
Clin Gastroenterol Hepatol. 2015 Dec;13(13):2360-8. (PMID: 26122767)
Nature. 2015 May 28;521(7553):452-9. (PMID: 26017444)
J Med Internet Res. 2024 May 22;26:e53164. (PMID: 38776130)
Nat Med. 2025 Jan;31(1):60-69. (PMID: 39779929)
Liver Int. 2026 Oct;46(10):e70879. (PMID: 42757445)
J Hepatol. 1990 Sep;11(2):272-6. (PMID: 2254635)
Hepatology. 2017 Oct;66(4):1275-1285. (PMID: 28543844)
Gastroenterology. 2015 Jun;148(7):1340-52.e7. (PMID: 25754159)
Hepatol Commun. 2020 Feb 20;4(4):518-526. (PMID: 32258947)
Liver Int. 2018 Jan;38(1):6-14. (PMID: 28771932)
J Hepatol. 2023 Mar;78(3):663-671. (PMID: 36283499)
Liver Int. 2025 Jun;45(6):e70115. (PMID: 40347005)
J Hepatol. 2019 Jun;70(6):1222-1261. (PMID: 30926241)
Gastroenterology. 2014 Jul;147(1):109-118.e5. (PMID: 24704526)
Clin Pharmacol Ther. 2011 Jun;89(6):806-15. (PMID: 21544079)
J Hepatol. 2022 Aug;77(2):293-295. (PMID: 35421424)
Hepatol Int. 2023 Dec;17(6):1626-1636. (PMID: 37188998)
Hepatol Res. 2020 Feb;50(2):224-232. (PMID: 31652370)
Scand J Gastroenterol. 2015 Apr;50(4):439-46. (PMID: 25639449)
Hepatol Int. 2023 Apr;17(2):488-498. (PMID: 36327052)
JAMA Netw Open. 2023 Oct 2;6(10):e2336483. (PMID: 37782499)
Grant Information: 82270618 National Natural Science Foundation of China; 2025ZD01906200 Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project
Contributed Indexing: Keywords: chemical and drug induced liver injury; chronic disease; clinical outcome; large language models; liver failure; prognosis
Local Abstract: [plain-language-summary] Drug‐induced liver injury (DILI) would progress to chronicity or death. We evaluate the utility of large language models (LLMs) in predicting DILI outcomes. We found GPT‐5.1 and Gemini‐2.5 Pro showed AUROCs approaching senior hepatologists for DILI outcomes with limited accuracy and specificity. LLM‐Rules demonstrated stable performance across cohorts with improved sensitivity or specificity, supporting the potential of multi‐LLM approaches as clinician‐supervised complementary tools.
Entry Date(s): Date Created: 20260918 Date Completed: 20260918 Latest Revision: 20261005
Update Code: 20261005
PubMed Central ID: PMC13586842
DOI: 10.1111/liv.70879
PMID: 42757445
Βάση Δεδομένων: MEDLINE
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  Data: Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug-Induced Liver Injury.
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  Data: <searchLink fieldCode="AU" term="%22Fu+H%22">Fu H</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Du+Y%22">Du Y</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Zhao+G%22">Zhao G</searchLink>; Department of Infectious Diseases, Ruijin North Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Xiao+Y%22">Xiao Y</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Song+S%22">Song S</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Zang+X%22">Zang X</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Zhou+T%22">Zhou T</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Zhuang+Y%22">Zhuang Y</searchLink>; Department of Infectious Diseases, Wuxi Xinrui Hospital, Shanghai Jiao Tong University School of Medicine, Wuxi, China.<br /><searchLink fieldCode="AU" term="%22Mo+R%22">Mo R</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Lai+R%22">Lai R</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Xie+Q%22">Xie Q</searchLink>; Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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  Data: <searchLink fieldCode="JN" term="%22101160857%22">Liver international : official journal of the International Association for the Study of the Liver</searchLink> [Liver Int] 2026 Oct; Vol. 46 (10), pp. e70879.
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  Data: Journal Article; Multicenter Study; Comparative Study
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley-Blackwell%22">Wiley-Blackwell </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101160857 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1478-3231 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2214783223%22">14783223 </searchLink><i>NLM ISO Abbreviation: </i>Liver Int <i>Subsets: </i>MEDLINE
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  Data: <searchLink fieldCode="MM" term="%22Chemical+and+Drug+Induced+Liver+Injury%22">Chemical and Drug Induced Liver Injury*</searchLink>/<searchLink fieldCode="MM" term="%22Chemical+and+Drug+Induced+Liver+Injury+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Chemical+and+Drug+Induced+Liver+Injury%22">Chemical and Drug Induced Liver Injury*</searchLink>/<searchLink fieldCode="MM" term="%22Chemical+and+Drug+Induced+Liver+Injury+mortality%22">mortality</searchLink> <br /><searchLink fieldCode="MM" term="%22Large+Language+Models%22">Large Language Models*</searchLink> <br /><searchLink fieldCode="MM" term="%22Gastroenterology%22">Gastroenterology*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Prognosis%22">Prognosis</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Middle+Aged%22">Middle Aged</searchLink> ; <searchLink fieldCode="MH" term="%22ROC+Curve%22">ROC Curve</searchLink> ; <searchLink fieldCode="MH" term="%22Clinical+Decision-Making%22">Clinical Decision-Making</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background & Aims: Drug-induced liver injury (DILI) would progress to chronicity or death. Large language models (LLMs) may enhance clinical decision-making, yet their utility relative to physicians in DILI remains unclear. Therefore, we evaluated their performance in predicting DILI outcomes.<br />Methods: We enrolled 943 DILI patients from three centers as internal and external cohorts. Based on 12-month follow-up, outcomes were classified as recovery, 6/12-month chronicity, and death. LLMs (Gemini-2.5 Pro, GPT-5.1, DeepSeek-3.2), hepatologists (Junior, middle, senior), and models (Hy's Law, nHy's Law, MELD Score) estimated probabilities of outcomes. LLM-Rules (VOTE, OR, AND) were applied to enhance stability. Model performance was assessed.<br />Results: For 6-month chronicity, the senior achieved highest AUROC (0.61) with an accuracy of 70%. Gemini-2.5 Pro and GPT-5.1 yielded AUROCs of 0.60 and 0.59, respectively, outperforming junior and middle hepatologists. Gemini-2.5 Pro demonstrated strongest agreement with senior (κ = 0.43). LLMs all exhibited lower accuracy and specificity than hepatologists. A similar result was observed in 12-month chronicity. For overall mortality, the senior achieved highest AUROC (0.87) with an accuracy of 83%. Gemini-2.5 Pro and GPT-5.1 achieved AUROCs of 0.86, outperforming junior hepatologist, Hy's Law, and nHy's Law. GPT-5.1 achieved strongest agreement with senior (κ = 0.25). LLM-Rules demonstrated stability for predicting outcomes across cohorts. OR and AND rules improved sensitivity and specificity, respectively.<br />Conclusions: GPT-5.1 and Gemini-2.5 Pro showed AUROCs approaching senior hepatologists for DILI outcomes with limited accuracy and specificity. LLM-Rules demonstrated stable performance across cohorts with improved sensitivity or specificity, supporting the potential of multi-LLM approaches as clinician-supervised complementary tools.<br /> (© 2026 The Author(s). Liver International published by John Wiley & Sons Ltd.)
– Name: Ref
  Label: References
  Group: RefInfo
  Data: Perspect Biol Med. 1968 Autumn;12(1):135-61. (PMID: <searchLink fieldCode="PM" term="%224387099%22">4387099)</searchLink><br />J Med Syst. 2025 May 15;49(1):64. (PMID: <searchLink fieldCode="PM" term="%2240372503%22">40372503)</searchLink><br />JHEP Rep. 2025 Sep 03;7(12):101579. (PMID: <searchLink fieldCode="PM" term="%2241321931%22">41321931)</searchLink><br />Clin Mol Hepatol. 2023 Jul;29(3):721-732. (PMID: <searchLink fieldCode="PM" term="%2236946005%22">36946005)</searchLink><br />Hepatology. 2010 Dec;52(6):2065-76. (PMID: <searchLink fieldCode="PM" term="%2220949552%22">20949552)</searchLink><br />J Hepatol. 2016 Sep;65(3):532-42. (PMID: <searchLink fieldCode="PM" term="%2227184533%22">27184533)</searchLink><br />Gastroenterology. 2019 Nov;157(5):1245-1252.e3. (PMID: <searchLink fieldCode="PM" term="%2231302142%22">31302142)</searchLink><br />Liver Int. 2019 Jan;39(1):31-41. (PMID: <searchLink fieldCode="PM" term="%2230003672%22">30003672)</searchLink><br />Hepatology. 2000 Apr;31(4):864-71. (PMID: <searchLink fieldCode="PM" term="%2210733541%22">10733541)</searchLink><br />Front Immunol. 2022 Apr 12;13:772368. (PMID: <searchLink fieldCode="PM" term="%2235493499%22">35493499)</searchLink><br />Liver Int. 2025 Apr;45(4):e16112. (PMID: <searchLink fieldCode="PM" term="%2239526465%22">39526465)</searchLink><br />Clin Gastroenterol Hepatol. 2015 Dec;13(13):2360-8. (PMID: <searchLink fieldCode="PM" term="%2226122767%22">26122767)</searchLink><br />Nature. 2015 May 28;521(7553):452-9. (PMID: <searchLink fieldCode="PM" term="%2226017444%22">26017444)</searchLink><br />J Med Internet Res. 2024 May 22;26:e53164. (PMID: <searchLink fieldCode="PM" term="%2238776130%22">38776130)</searchLink><br />Nat Med. 2025 Jan;31(1):60-69. (PMID: <searchLink fieldCode="PM" term="%2239779929%22">39779929)</searchLink><br />Liver Int. 2026 Oct;46(10):e70879. (PMID: <searchLink fieldCode="PM" term="%2242757445%22">42757445)</searchLink><br />J Hepatol. 1990 Sep;11(2):272-6. (PMID: <searchLink fieldCode="PM" term="%222254635%22">2254635)</searchLink><br />Hepatology. 2017 Oct;66(4):1275-1285. (PMID: <searchLink fieldCode="PM" term="%2228543844%22">28543844)</searchLink><br />Gastroenterology. 2015 Jun;148(7):1340-52.e7. (PMID: <searchLink fieldCode="PM" term="%2225754159%22">25754159)</searchLink><br />Hepatol Commun. 2020 Feb 20;4(4):518-526. (PMID: <searchLink fieldCode="PM" term="%2232258947%22">32258947)</searchLink><br />Liver Int. 2018 Jan;38(1):6-14. (PMID: <searchLink fieldCode="PM" term="%2228771932%22">28771932)</searchLink><br />J Hepatol. 2023 Mar;78(3):663-671. (PMID: <searchLink fieldCode="PM" term="%2236283499%22">36283499)</searchLink><br />Liver Int. 2025 Jun;45(6):e70115. (PMID: <searchLink fieldCode="PM" term="%2240347005%22">40347005)</searchLink><br />J Hepatol. 2019 Jun;70(6):1222-1261. (PMID: <searchLink fieldCode="PM" term="%2230926241%22">30926241)</searchLink><br />Gastroenterology. 2014 Jul;147(1):109-118.e5. (PMID: <searchLink fieldCode="PM" term="%2224704526%22">24704526)</searchLink><br />Clin Pharmacol Ther. 2011 Jun;89(6):806-15. (PMID: <searchLink fieldCode="PM" term="%2221544079%22">21544079)</searchLink><br />J Hepatol. 2022 Aug;77(2):293-295. (PMID: <searchLink fieldCode="PM" term="%2235421424%22">35421424)</searchLink><br />Hepatol Int. 2023 Dec;17(6):1626-1636. (PMID: <searchLink fieldCode="PM" term="%2237188998%22">37188998)</searchLink><br />Hepatol Res. 2020 Feb;50(2):224-232. (PMID: <searchLink fieldCode="PM" term="%2231652370%22">31652370)</searchLink><br />Scand J Gastroenterol. 2015 Apr;50(4):439-46. (PMID: <searchLink fieldCode="PM" term="%2225639449%22">25639449)</searchLink><br />Hepatol Int. 2023 Apr;17(2):488-498. (PMID: <searchLink fieldCode="PM" term="%2236327052%22">36327052)</searchLink><br />JAMA Netw Open. 2023 Oct 2;6(10):e2336483. (PMID: <searchLink fieldCode="PM" term="%2237782499%22">37782499)</searchLink>
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  Data: 82270618 National Natural Science Foundation of China; 2025ZD01906200 Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project
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  Data: <i>Keywords: </i>chemical and drug induced liver injury; chronic disease; clinical outcome; large language models; liver failure; prognosis<br /><i>Local Abstract: </i>[plain-language-summary] Drug‐induced liver injury (DILI) would progress to chronicity or death. We evaluate the utility of large language models (LLMs) in predicting DILI outcomes. We found GPT‐5.1 and Gemini‐2.5 Pro showed AUROCs approaching senior hepatologists for DILI outcomes with limited accuracy and specificity. LLM‐Rules demonstrated stable performance across cohorts with improved sensitivity or specificity, supporting the potential of multi‐LLM approaches as clinician‐supervised complementary tools.
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        Type: general
      – SubjectFull: Prognosis
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      – SubjectFull: Male
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      – SubjectFull: Chemical and Drug Induced Liver Injury diagnosis
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      – SubjectFull: Chemical and Drug Induced Liver Injury mortality
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      – SubjectFull: Large Language Models
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      – SubjectFull: Gastroenterology
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      – TitleFull: Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug-Induced Liver Injury.
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              Text: 2026 Oct
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              Value: 10
          Titles:
            – TitleFull: Liver international : official journal of the International Association for the Study of the Liver
              Type: main
ResultId 1