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

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; G. Goh; S. S. Hamid; D. Q. Huang; T. Kawaguchi; W. Kim; S. U. Kim; J. D. Jia; C. J. 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; Y. Yilmaz; F. Yilmaz; M. L. Yu; K. Zalata; M. H. Zheng; J. G. Fan; S. K. Sarin; V. Wong; J. George

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

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
, G. Goh
, S. S. Hamid
, D. Q. Huang
, T. Kawaguchi
, W. Kim
, S. U. Kim
, J. D. Jia
, C. J. 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, Y. Yilmaz, F. Yilmaz, M. L. Yu, K. Zalata, M. H. Zheng, J. G. Fan, S. K. Sarin, V. Wong, J. George

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Journal	Hepatology International
DOIs	https://doi.org/10.1007/s12072-026-11092-6
Publication status	Accepted/In press - 2026

Keywords

Artificial Intelligence
Histopathology
MAFLD
MASH

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10.1007/s12072-026-11092-6

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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. (Accepted/In press). 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

@article{823d295e74724eaba908f3eaf28f6b94,

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",

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.",

keywords = "Artificial Intelligence, Histopathology, MAFLD, MASH",

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

note = "Publisher Copyright: {\textcopyright} The Author(s) 2026.",

year = "2026",

doi = "10.1007/s12072-026-11092-6",

language = "English (US)",

journal = "Hepatology International",

issn = "1936-0533",

publisher = "Springer",

}

Elangovan, H, Akbary, K, Rastogi, A, Wee, A, Soon, G, Adams, L, Carr-Boyd, E, Clouston, A, Cooper, CL, Chan, WK, Dan, YY, Dela-Cruz, R, Goh, G, Hamid, SS, Huang, DQ, Kawaguchi, T, Kim, W, Kim, SU, Jia, JD, Liu, CJ, Liu, F, Leow, WQ, Muthiah, MD, Ng, I, Payawal, D, Pan, AF, Pervez, S, Shiha, G, Tanwandee, T, Tanaka, Y, Thiyaphat, L, Vij, M, Yilmaz, Y, Yilmaz, F, Yu, ML, Zalata, K, Zheng, MH, Fan, JG, Sarin, SK, Wong, V & 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

TY - JOUR

T1 - 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

AU - Elangovan, H.

AU - Akbary, K.

AU - Rastogi, A.

AU - Wee, A.

AU - Soon, G.

AU - Adams, L.

AU - Carr-Boyd, E.

AU - Clouston, A.

AU - Cooper, C. L.

AU - Chan, W. K.

AU - Dan, Y. Y.

AU - Dela-Cruz, R.

AU - Goh, G.

AU - Hamid, S. S.

AU - Huang, D. Q.

AU - Kawaguchi, T.

AU - Kim, W.

AU - Kim, S. U.

AU - Jia, J. D.

AU - Liu, C. J.

AU - Liu, F.

AU - Leow, W. Q.

AU - Muthiah, M. D.

AU - Ng, I.

AU - Payawal, D.

AU - Pan, A. F.

AU - Pervez, S.

AU - Shiha, G.

AU - Tanwandee, T.

AU - Tanaka, Y.

AU - Thiyaphat, L.

AU - Vij, M.

AU - Yilmaz, Y.

AU - Yilmaz, F.

AU - Yu, M. L.

AU - Zalata, K.

AU - Zheng, M. H.

AU - Fan, J. G.

AU - Sarin, S. K.

AU - Wong, V.

AU - George, J.

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Histopathology

KW - MAFLD

KW - MASH

UR - https://www.scopus.com/pages/publications/105038670270

U2 - 10.1007/s12072-026-11092-6

DO - 10.1007/s12072-026-11092-6

M3 - Article

AN - SCOPUS:105038670270

SN - 1936-0533

JO - Hepatology International

JF - Hepatology International

ER -

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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