Machine Learning for Hand Surgeons: Emerging Clinical Applications.

Abstract

Machine learning (ML) is transforming medicine and holds substantial potential in hand surgery to manage complex conditions, predict surgical outcomes, and optimize resources. Emerging ML applications in hand surgery include diagnostic imaging interpretation, risk stratification, outcome prediction, and practice management. For example, ML algorithms predict patient outcomes after procedures such as carpal tunnel release and optimize surgical scheduling to reduce wait times. We emphasize the importance of appraising ML research quality, using previously published guidelines to evaluate predictive models. We address challenges such as data quality and bias, the "black box" nature of some models, legal and ethical concerns, limited generalizability across populations, and the risk of disproportionate benefits favoring well-studied groups. To advance ML in hand surgery, surgeons should collaborate to generate diverse, high-quality data sets, reducing bias and improving generalizability. Developing transparent, explainable algorithms will enhance clinician trust and understanding. Furthermore, integrating ML into clinical workflows through decision support tools will facilitate evidence-based, individualized care. By engaging with these technologies, hand surgeons can help shape the future of the specialty, leading to better patient outcomes and optimized resource utilization.