Artificial Intelligence for Predicting Postoperative Complications in Orthopedics: A Review of Clinical Applications, Challenges, and Future Directions

Postoperative complications, including infections, venous thromboembolism (VTE), and prolonged length of stay (LOS), remain major sources of morbidity and healthcare expenditure in orthopedic surgery. While traditional risk stratification tools provide useful benchmarks, they often fall short in delivering precise, individualized predictions. This review extends prior work by providing a direct comparative synthesis of artificial intelligence (AI) and traditional statistical models in orthopedics, while proposing a roadmap of the Validation, Integration, and Regulation (VIR) framework for responsible adoption, emphasizing multicenter validation, workflow-integrated deployment, and appropriate regulatory oversight to support responsible translation. This narrative review synthesizes recent advances in the use of AI and machine learning (ML) models for forecasting postoperative complications in orthopedic surgery. We conducted a structured narrative (non-systematic) review, following SANRA (Scale for the Assessment of Narrative Review Articles) recommendations, of peer-reviewed studies published from January 1, 2017, to July 1, 2025, in PubMed, Scopus, and Google Scholar. Eligible articles involved adult or pediatric orthopedic surgical populations, developed, validated, or applied AI/ML models to predict perioperative or postoperative complications, and reported quantitative performance metrics (e.g., discrimination, calibration, or clinical impact). Imaging-only diagnostic studies, non-orthopedic or non-surgical cohorts, and non-original reports (reviews, editorials, conference abstracts) were excluded. Given heterogeneity in endpoints and validation designs, we performed a structured narrative synthesis without meta-analysis. We also conducted a Prediction model Risk Of Bias ASsessment Tool (PROBAST)-informed, domain-based appraisal for the subset of primary prediction-model studies contributing to the comparative performance synthesis. AI-driven predictive models often outperform classical statistical methods across outcomes, including prosthetic joint infection, transfusion, implant failure, and nonunion, with reported area under the curve (AUC) values typically in the 0.75-0.90 range for AI/ML models, compared to 0.60-0.75 for traditional regression across the studies reviewed. These comparisons should be interpreted in light of heterogeneity in datasets, endpoints, and validation design, and AUC alone may not capture clinical utility for low-prevalence events without calibration and threshold-based evaluation. Adoption remains constrained by overfitting, limited multicenter validation, inconsistent calibration/utility reporting, explainability, and interoperability challenges. Future work should pursue federated learning, hybrid clinician-AI frameworks, and equity-focused validation to responsibly integrate AI into orthopedic surgical care.