Non-invasive tests developed using artificial intelligence for screening in MASLD: interest and pitfalls

Jerome Boursier^{1

2}, Bertrand Cariou³

Affiliations

¹ Service d'Hépato-Gastroentérologie et Oncologie Digestive, Hôpital Universitaire d'Angers, Angers, France.
² Laboratoire HIFIH, SFR ICAT 4208, Université d'Angers, Angers, France.
³ Nantes Université, CHU Nantes, CNRS, Inserm, l'institut du thorax, F-44000 Nantes, France.

Editorial

Jerome Boursier et al. J Clin Endocrinol Metab. 2025.

. 2025 May 19:dgaf294.

doi: 10.1210/clinem/dgaf294. Online ahead of print.

Jerome Boursier^{1

2}, Bertrand Cariou³

¹ Service d'Hépato-Gastroentérologie et Oncologie Digestive, Hôpital Universitaire d'Angers, Angers, France.
² Laboratoire HIFIH, SFR ICAT 4208, Université d'Angers, Angers, France.
³ Nantes Université, CHU Nantes, CNRS, Inserm, l'institut du thorax, F-44000 Nantes, France.

No abstract available

Keywords: MASLD; artificial intelligence; fibrosis; non-invasive tests; steatosis.

Comment on

Machine Learning-Based Biomarker Identification for Early Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease.
Boullion J, Husein A, Agrawal A, Xing D, Hossain MI, Bhuiyan MS, Rom O, Conrad SA, Vanchiere JA, Orr AW, Kevil CG, Bhuiyan MAN. Boullion J, et al. J Clin Endocrinol Metab. 2025 Feb 21:dgaf111. doi: 10.1210/clinem/dgaf111. Online ahead of print. J Clin Endocrinol Metab. 2025. PMID: 39980343 Free PMC article.