Artificial Intelligence in the Diagnosis and Prognostication of the Musculoskeletal Patient

Miguel M Girod; Sami Saniei; Marisa N Ulrich; Lainey G Bukowiec; Kellen L Mulford; Michael J Taunton; Cody C Wyles

doi:10.1177/15563316251339660

Artificial Intelligence in the Diagnosis and Prognostication of the Musculoskeletal Patient

HSS J. 2025 May 28;21(3):15563316251339660. doi: 10.1177/15563316251339660. Online ahead of print.

Authors

Miguel M Girod¹, Sami Saniei¹, Marisa N Ulrich^{1

2}, Lainey G Bukowiec^{1

2}, Kellen L Mulford¹, Michael J Taunton^{1

2}, Cody C Wyles^{1

2}

Affiliations

¹ Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.
² Mayo Clinic Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.

PMID: 40454292
PMCID: PMC12119539 (available on 2026-05-28)
DOI: 10.1177/15563316251339660

Abstract

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

Keywords: artificial intelligence; computer vision; deep learning; diagnosis and prognosis; generative AI; machine learning; musculoskeletal health; orthopedic surgery.

Publication types

Review