The Asian Pacific Association of the Study of the Liver expert survey on artificial intelligence-assisted reporting of liver histopathology in metabolic dysfunction associated fatty liver disease

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Authors: H. Elangovan, K. Akbary, A. Rastogi, A. Wee, G. Soon, L. Adams, E. Carr-Boyd, A. Clouston, C. L. Cooper, W. K. Chan, Y.Y. Dan, R. Dela-Cruz
+30 more G. Goh, S. S. Hamid, D. Q. Huang, T. Kawaguchi, W. Kim, S. U. Kim, S. U. Kim, J. D. Jia, F. H. Liu, F. Liu, W. Q. Leow, M. D. Muthiah, I. Ng, D. Payawal, A. F. Pan, S. Pervez, G. Shiha, T. Tanwandee, Y. Tanaka, L. Thiyaphat, M. Vij, F. Yilmaz, F. Yilmaz, M. L. Yu, K. Zalata, M. H. Zheng, J.-G. Fan, Shiv Sarin, V. Wong, Jacob George
Cited by: 1
References: 6

Abstract

INTRODUCTION: Artificial intelligence (AI) and digital pathology have the potential to augment liver biopsy interpretation in MAFLD in clinical practice and trials assessment. However, attitudes and barriers to its implementation have not been systematically explored. METHODS: A survey focusing on conventional liver histology, digital pathology and its AI applications in MAFLD/MASH was conducted among hepatologists and liver pathologists in the Asia Pacific region. RESULTS: AI-assisted digital pathology is perceived to be a valuable addition to existing histological reporting in MAFLD/MASH. Defined standards for application and validation of AI models are important priorities for their implementation. CONCLUSION: There is consensus among clinical experts in the Asia Pacific that AI-assisted histological assessment is useful in MAFLD/MASH interpretation. However, there remain important challenges to the adoption of these technologies into routine clinical workflows.

Fields of Study & Subjects

Liver Disease Diagnosis and Treatment AI in cancer detection Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Hepatology International
Publisher
Springer Science and Business Media LLC
Volume
20
Issue
3
Pages
539–549
Publication date
13 May 2026
Language
EN