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

Elangovan, H.; Akbary, K.; Rastogi, A.; Wee, A.; Soon, G.; Adams, L.; Carr-Boyd, E.; Clouston, A.; Cooper, C.L.; Chan, W.K.; Dan, Y.Y.; Dela-Cruz, R.; Goh, G.; Hamid, S.S.; Huang, D.Q.; Kawaguchi, T.; Kim, W.; Kim, S.U.; Jia, J.D.; Liu, C.J.; Liu, F.; Leow, W.Q.; Muthiah, M.D.; Ng, I.; Payawal, D.; Pan, A.F.; Pervez, S.; Shiha, G.; Tanwandee, T.; Tanaka, Y.; Thiyaphat, L.; Vij, M.; YILMAZ, YUSUF; Yilmaz, FATİH; Yu, M.L.; Zalata, K.; Zheng, M.H.; Fan, J.G.; Sarin, S.K.; Wong, V.; George, J.

doi:10.1007/s12072-026-11092-6

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

Elangovan H., Akbary K., Rastogi A., Wee A., Soon G., Adams L., ...Daha Fazla

Hepatology International, 2026 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Basım Tarihi: 2026
Doi Numarası: 10.1007/s12072-026-11092-6
Dergi Adı: Hepatology International
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE
Anahtar Kelimeler: Artificial Intelligence, Histopathology, MAFLD, MASH
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

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.