Artificial Intelligence in Orthopaedics: Current Evidence and Clinical Translation Across the Patient Care Pathway - Rafael De Nigris González; Priscila Luiza Mello (Journal of Clinical Medicine, 2026)
Background: Artificial intelligence (AI) has expanded rapidly across orthopaedic practice, yet routine clinical adoption remains limited despite strong technical performance. This narrative review examines why a persistent gap separates technical maturity from clinical maturity across the orthopaedic patient care pathway. Methods: We performed a structured qualitative evidence synthesis of contemporary high-level evidence (systematic reviews, diagnostic test accuracy meta-analyses, and structured narrative reviews) retrieved from PubMed/MEDLINE, Scopus, and Web of Science, supplemented by backward screening of reference lists, covering January 2022 to June 2026. Twenty-one evidence syntheses were analysed thematically across six predefined analytical domains and organized according to the orthopaedic patient pathway. Reporting followed the SANRA (Scale for the Assessment of Narrative Review Articles) criteria. Results: Musculoskeletal imaging and fracture detection represented the most mature domains, with several applications reaching early clinical adoption. Applications in arthroplasty planning, shoulder surgery, perioperative prediction, multimodal AI, and clinical decision support remained at developing or emerging stages. Recurrent barriers included limited external validation, dataset heterogeneity, poor workflow interoperability, limited explainability, regulatory and ethical uncertainty, and scarce patient-centred outcome evidence. Conclusions: The principal challenge facing orthopaedic AI is no longer algorithm development but clinical translation. We propose the ORION Clinical Readiness Framework, an evidence-informed five-domain model describing the transition from Technical Performance through Clinical Validation, Workflow Integration, and Patient Benefit to Routine Clinical Adoption, to guide implementation and future research.