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Large language models for clinical decision support in gastroenterology and hepatology

Nature Reviews Gastroenterology & Hepatology (2025)Cite this article

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Abstract

Clinical decision making in gastroenterology and hepatology has become increasingly complex and challenging for physicians. This growing complexity can be addressed by computational tools that support clinical decisions. Although numerous clinical decision support systems (CDSS) have emerged, they have faced difficulties with real-world performance and generalizability, resulting in limited clinical adoption. Generative artificial intelligence (AI), particularly large language models (LLMs), are introducing new possibilities for CDSS by offering more flexible and adaptable support that better reflects complex clinical scenarios. LLMs can process unstructured text, including patient data and medical guidelines, and integrate various information sources with high accuracy, especially when augmented with retrieval-augmented generation. Thus, LLMs can provide dynamic, context-specific support by generating personalized treatment recommendations, identifying potential complications based on patient history, and enabling natural language interactions with health-care providers. However, important challenges persist, particularly regarding biases, hallucinations, interoperability barriers, and proper training of health-care providers. We examine the parallel evolution of the complexity in clinical management in gastroenterology and hepatology, and the technical developments leading to current generative AI models. We discuss how these advances are converging to create effective CDSS, providing a conceptual basis for further development and clinical adoption of these systems.

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Fig. 1: Evolution of clinical decision support systems and cases of use in gastroenterology and hepatology.
Fig. 2: Stages of clinical decision making and opportunities for LLM-based decision support.
Fig. 3: Successful implementation of LLMs in clinical decision support systems.

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Acknowledgements

J.N.K. discloses support for the research and publication of this work from the EU’s Horizon Europe Research and Innovation programme (GENIAL, 101096312) and the European Research Council ERC (NADIR, grant number 101114631). J.C. discloses support for the research for this work from the Mildred-Scheel-Postdoktorandenprogramm of the German Cancer Aid (grant number 70115730).

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Authors and Affiliations

  1. Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

    Isabella Catharina Wiest, Jan Clusmann, Carolin V. Schneider & Jakob Nikolas Kather

  2. Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Isabella Catharina Wiest

  3. German Cancer Consortium (DKTK) partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Isabella Catharina Wiest & Jan Clusmann

  4. Ajmera Transplant Centre, University Health Network, Toronto, Ontario, Canada

    Mamatha Bhat

  5. Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

    Jan Clusmann & Carolin V. Schneider

  6. Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Xiaofeng Jiang

  7. Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

    Jakob Nikolas Kather

  8. Medical Oncology, National Center for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

    Jakob Nikolas Kather

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Correspondence to Jakob Nikolas Kather.

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Competing interests

J.N.K. declares consulting services for Bioptimus, Owkin, DoMore Diagnostics, Panakeia, AstraZeneca, Mindpeak and MultiplexDx; holds shares in StratifAI and Synagen; has received a research grant from GSK; and has received honoraria from AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, Merck, MSD, BMS, Roche, Pfizer and Fresenius. I.C.W. has received honoraria from AstraZeneca. All other authors declare no competing interests.

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Wiest, I.C., Bhat, M., Clusmann, J. et al. Large language models for clinical decision support in gastroenterology and hepatology. Nat Rev Gastroenterol Hepatol (2025). https://doi.org/10.1038/s41575-025-01108-1

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