The Use of AI in Hepatology

Progress and challenges in the use of AI

AI systems have rapidly evolved over the past 15 years, with major advances particularly in image-based applications in radiology and pathology – and more recently, also in language-based methods. Despite this progress, clinical use of AI remains limited due to numerous implementation challenges. To better understand these barriers and support the integration of AI tools into hepatology, the EASL AI Task Force conducted a structured consensus process involving expert hepatologists and data scientists, as well as clinical AI and regulatory sciences experts. They based it on the Delphi method – a systematic, iterative, anonymized survey procedure widely used in medicine to develop guidelines. It ensures that resulting recommendations reflect broad expert consensus. Dr. Jan Clusmann, first author of the publication and postdoctoral researcher in the “Clinical Artificial Intelligence” group led by Prof. Jakob N. Kather at the EKFZ for Digital Health at TU Dresden, coordinated the entire process and compiled the results.

AI literacy as a key to implementation

A central recommendation of the group is to build AI literacy among healthcare professionals. The expert panel further advocates that AI systems must demonstrate efficacy, reliability, and trustworthiness before they can be adopted into clinical practice. Even validated tools often face practical barriers due to heterogenous infrastructure across healthcare systems and limited interoperability of hospital IT systems. Addressing these implementation challenges is crucial to enabling routine use.

Conducting AI-supported clinical trials and facilitating data exchange

The experts also highlight the importance of investigating AI methods in clinical trials. This requires seamless information exchange between institutions, decentralized access to de-identified data, and early collaboration between AI researchers and clinical trial investigators

Promoting interdisciplinary collaboration and creating structured frameworks

Professional societies can help, for example by establishing targeted initiatives and structured frameworks to support AI implementation. Future clinical guidelines, coordinated by these societies, should explicitly discuss recommendations of AI models. Those guidelines should further serve as prioritized clinical context for AI-based decision support systems.

The position paper “The barriers for uptake of artificial intelligence in hepatology and how to overcome them,” is published in the Journal of Hepatology, the official journal of the European Association for the Study of the Liver (EASL).

About the EASL AI Task Force

The EASL AI Task Force is a group of experts established by the European Association for the Study of the Liver (EASL) to guide the responsible and effective use of artificial intelligence in hepatology. Their goal is to harness AI to improve research, education, clinical decision-making, and patient care related to liver diseases. They also advise on AI integration for EASL events and publications as well as fostering strategic partnerships to drive AI advancements relevant to liver diseases and supporting operational tasks at EASL. Further information and list of members: https://easl.eu/easl/leadership-and-governance/ai-task-force/