The barriers to uptake of artificial intelligence in hepatology and how to overcome them

Abstract

Inteligencia artificial; Consenso; Hepatología

Artificial intelligence; Consensus; Hepatology

Intel·ligència artificial; Consens; Hepatologia

Artificial intelligence (AI) methods in hepatology have proliferated since the mid-2010s, with numerous publications and some regulatory approvals. Yet, adoption of AI methods in real-world clinical practice and clinical research remains limited. Despite clear benefits of using AI to analyse complex data types in hepatology, such as histopathology, radiology images, multi-omics and more recently, natural language patient data, there are still substantial barriers and challenges to its integration into routine clinical workflows. In this position paper, we assess limitations and propose a set of clear recommendations aimed at both the development of AI systems and the broader hepatology environment to facilitate the transition of AI-based diagnostic, prognostic, and predictive tools into clinical care. In particular, we argue that the use of AI in clinical trials, seamless integration into hospital information systems and building AI literacy among clinicians will ultimately drive clinical adoption. We validate this perspective through a Delphi consensus involving 34 international experts from hepatology, AI, and data science, ensuring a comprehensive and consensus-driven evaluation of our recommendations.

JC is supported by the Mildred-Scheel-Postdoktorandenprogramm of the German Cancer Aid (grant #70115730). JNK is supported by the German Cancer Aid (DECADE, 70115166), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; TRANSFORM LIVER, 031L0312A; TANGERINE, 01KT2302 through ERA-NET Transcan; Come2Data, 16DKZ2044A; DEEP-HCC, 031L0315A; DECIPHER-M, 01KD2420A; NextBIG, 01ZU2402A), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (TransplantKI, 01VSF21048), the European Union’s Horizon Europe research and innovation programme (ODELIA, 101057091; GENIAL, 101096312), the European Research Council (ERC; NADIR, 101114631), the National Institutes of Health (EPICO, R01 CA263318) and the National Institute for Health and Care Research (NIHR, NIHR203331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. TL was funded by the German Cancer Aid (Deutsche Krebshilfe - DECADE 70115166), the Federal Ministry of Education and Research (BMBF - TRANSFORM LIVER 031L0312B) and the Federal Ministry of Health (BMG - DEEP LIVER 2520DAT111). C.V.S is supported by a grant from the Interdisciplinary Centre for Clinical Research within the faculty of Medicine at the RWTH Aachen University (PTD 1-13/IA 532313), the Junior Principal Investigator Fellowship program of RWTH Aachen Excellence strategy and the NRW Rueckkehr Programme of the Ministry of Culture and Science of the German State of North Rhine-Westphalia, and the CRC 1382 project A11 and B09 funded by Deutsche Forschungsgesellschaft (DFG, German Research Foundation) – Project-ID 403224013 – SFB 1382“.