Machine Learning in Hand Pathology Diagnosis: A Narrative Review of Current Insights.

Authors

Bryan Lim, Ishith Seth, Richard J Ross, Warren M Rozen

Published in

Hand (New York, N.Y.). Pages 15589447261493988. Oct 16, 2026. Epub Oct 16, 2026.

Abstract

Machine learning (ML), a subset of artificial intelligence (AI), is revolutionizing health care. Given the intricate anatomy and functional complexity of the hand, AI applications in hand pathology are gaining prominence, particularly in the diagnosis of fractures, nerve injuries, and musculoskeletal disorders. This review summarizes ML advancements in hand pathology diagnosis, enhancing clinical history analysis, examination findings interpretation, and imaging assessment. A systematic search of PubMed, Google Scholar, EMBASE, Cochrane CENTRAL, Scopus, and Web of Science from 1901 to March 2025 was conducted, focusing on ML applications in diagnosing hand fractures, tendon injuries, and nerve compressions. The inclusion criteria encompassed original research, systematic reviews, meta-analyses, and observational studies that demonstrated the use of AI in diagnostic imaging, preoperative assessment, or postoperative management. Studies that did not involve AI-driven diagnostic applications or lacked sufficient methodological transparency were excluded. Machine learning models, particularly convolutional neural networks (CNNs), have demonstrated high diagnostic accuracy in detecting fractures. Studies have validated CNNs in the detection of distal radius and ulna fractures, outperforming trainees and urgent care physicians. In addition, AI has enhanced clinical examination by integrating wearable motion sensors for objective joint assessment. However, challenges such as external validation, interpretability, and integration into clinical workflows remain, limiting broader clinical adoption. Machine learning shows promise in hand pathology diagnosis, but widespread adoption requires improved external validation, standardized methods, and interdisciplinary collaboration. Future research should prioritize multicentric studies and explainable AI to enhance trust and integration.

PMID:
42885855
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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