Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review.
Authors
Affiliations (5)
Affiliations (5)
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, No. 138 Xianlin Rd., Nanjing, 210023, China. Electronic address: [email protected].
- School of Integrative Medicine, Nanjing University of Chinese Medicine, No. 138 Xianlin Rd., Nanjing, 210023, China. Electronic address: [email protected].
- School of Integrative Medicine, Nanjing University of Chinese Medicine, No. 138 Xianlin Rd., Nanjing, 210023, China. Electronic address: [email protected].
- School of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, Jiangsu, 210023, China; Health & Weight Management Center, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210022, China. Electronic address: [email protected].
- School of Integrative Medicine, Nanjing University of Chinese Medicine, No. 138 Xianlin Rd., Nanjing, 210023, China. Electronic address: [email protected].
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) poses significant diagnostic challenges. We conducted a systematic review of artificial intelligence (AI) applications in MASLD diagnosis, focusing on predictive modeling, screening, imaging analysis, and therapeutic assistance. Following PRISMA guidelines, we searched five databases (Web of Science, SciFinder, Elsevier ScienceDirect, EBSCOhost, Springer) for studies published January 2020-January 2026. Of 1247 initially identified records, 191 studies met inclusion criteria. AI models demonstrate high diagnostic performance across multiple modalities. Deep learning analysis of US, CT, and MRI enables automated steatosis quantification and fibrosis staging with accuracy comparable to expert interpretation. AI integration with multi-omics data facilitates novel biomarker discovery and therapeutic target identification. Despite these advances, model generalisability, standardization, and clinical integration remain challenges. AI holds substantial potential to transform MASLD diagnostics, but prospective validation and implementation strategies are needed before widespread clinical adoption.