Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol.

The review approach involves a comprehensive search across three major academic databases for relevant literature. Investigators will screen articles published in five distinct languages to ensure a broad international perspective. Two independent reviewers will evaluate every identified reference based on predefined eligibility criteria. The team follows established PRISMA guidelines to maintain high standards of transparency and reproducibility. Researchers will specifically target studies focusing on human musculoskeletal injuries and surgical interventions. Any investigation failing to provide clinical data or involving animal subjects will be excluded from the final analysis. The protocol mandates registration on the PROSPERO database before the formal commencement of the study. This structured design ensures that the final synthesis remains focused on practical clinical applications.

Key findings from the literature are expected to reveal a significant prevalence of uncontrolled studies regarding digital diagnostic tools. The authors anticipate identifying a smaller subset of papers that describe actual clinical care outcomes. This review will compare traditional diagnostic gold standards against those utilizing automated algorithmic support. The study intends to map the current progress of digital intelligence in surgical decision-making. Researchers expect to find evidence regarding the prediction of post-operative complications and recovery trajectories. The synthesis will exclude meta-analyses to focus on primary research and case reports. Findings will characterize the current maturity level of these technologies within the orthopedic field. The final report will summarize the state of evidence as of September 2020.

The authors propose that this review will clarify the current status of digital intelligence in surgical practice. They expect to identify a high volume of preliminary research rather than robust clinical trials. This synthesis will highlight the disparity between theoretical models and practical patient care applications. The team anticipates that findings will underscore the necessity for more rigorous cohort studies. Future efforts should prioritize large-scale randomized trials to validate these automated diagnostic tools. This review will provide a baseline for assessing the reliability of predictive algorithms in surgery. The authors intend to map out how these technologies influence post-operative complication rates. This work serves as a foundational step toward integrating advanced computation into standard orthopedic workflows.