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

Authors

• H. Elangovan, The University of Sydney
• K. Akbary, HistoIndex Pte Ltd, Singapore
• A. Rastogi, Institute of Liver and Biliary Sciences
• A. Wee, National University Hospital
• G. Soon, Singapore General Hospital
• L. Adams, University of Western Australia
• E. Carr-Boyd, Auckland City Hospital
• A. Clouston, University of Queensland
• C. L. Cooper, Princess Alexandra Hospital
• W. K. Chan, University of Malaya
• Y. Y. Dan, National University Hospital
• R. Dela-Cruz, The Medical City, Ortigas
• G. Goh, Singapore General Hospital
• Saeed Hamid, Aga Khan UniversityFollow
• D. Q. Huang, National University Hospital
• T. Kawaguchi, Kurume University School of Medicine
• W. Kim, Seoul National University College of Medicine
• S. U. Kim, Yonsei University College of Medicine
• Shahid Pervez, Aga Khan UniversityFollow
• J. D. Jia, Capital Medical University

Document Type

Article

Department

Medicine; Pathology and Laboratory Medicine

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.

Comments

Volume, issue, and pagination are mot provided by the auhthor/publisher.

Publication (Name of Journal)

Hepatology International

DOI

10.1007/s12072-026-11092-6

Recommended Citation

Elangovan, H., Akbary, K., Rastogi, A., Wee, A., Soon, G., Adams, L., Carr-Boyd, E., Clouston, A., Cooper, C., Chan, W., Dan, Y., Dela-Cruz, R., Goh, G., Hamid, S., Huang, D., Kawaguchi, T., Kim, W., Kim, S., Pervez, S., Jia, J. (2026). 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. Hepatology International.
Available at: https://ecommons.aku.edu/pakistan_fhs_mc_med_gastroenterol/382