Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified - described as 'high diagnostic performance'

• Primary Outcome: Diagnostic performance of AI models for MASLD across multiple imaging modalities

• Nnt Or Sensitivity: Accuracy described as 'comparable to expert interpretation' but specific sensitivity/specificity values not provided

• Confidence Interval: Not reported

Clinical Application

Limited by current challenges in model generalisability, standardization, and clinical integration as identified by authors Relevant to Australian practice given MASLD prevalence, but TGA approval and PBS considerations for AI diagnostic tools would be required. Integration with existing imaging infrastructure and RACGP guidelines for liver disease screening would need consideration Patients requiring MASLD diagnosis across primary care and specialist hepatology settings

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.

References

1. 1.Wang, R., Liu, C., Xue, M., Qian, J., & Hu, Y. (2026). Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review. Computers in Biology and Medicine, 111619. https://doi.org/10.1016/j.compbiomed.2026.111619