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
| dc.contributor.author | Elangovan H. | |
| dc.contributor.author | Akbary K. | |
| dc.contributor.author | Rastogi A. | |
| dc.contributor.author | Wee A. | |
| dc.contributor.author | Soon G. | |
| dc.contributor.author | Yılmaz, Yusuf | |
| dc.contributor.author | George J. | |
| dc.date.accessioned | 2026-06-09T08:20:54Z | |
| dc.date.issued | 2026 | |
| dc.department | RTEÜ, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü | |
| dc.description.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. | |
| dc.identifier.citation | 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., . . . George, 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. https://doi.org/10.1007/s12072-026-11092-6 | |
| dc.identifier.doi | 10.1007/s12072-026-11092-6 | |
| dc.identifier.issn | 1936-0533 | |
| dc.identifier.scopus | 2-s2.0-105038670270 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://doi.org/10.1007/s12072-026-11092-6 | |
| dc.identifier.uri | https://hdl.handle.net/11436/13025 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Yılmaz, Yusuf | |
| dc.institutionauthorid | 0000-0003-4518-5283 | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Hepatology International | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Histopathology | |
| dc.subject | MAFLD | |
| dc.subject | MASH | |
| dc.title | 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 | |
| dc.type | Article |











