Comprehensive Summary
The literature review evaluates the effectiveness of an AI-powered framework in verifying anterior cruciate ligament (ACL) injury reports in professional footballers using public databases. The researchers developed a system that combines Google Programmable Search Engine and OpenAI’s GPT to search, translate, and analyze ACL tear-related information. The framework was validated by cross-checking ACL tear data from Transfermarkt. A total of 231 athletes were evaluated, which yielded 1546 search result items (SRIs). Among these athletes, 335 SRIs explicitly mentioned an ACL tear, corresponding to 83 confirmed ACL injuries. The AI-powered framework achieved a specificity of 99.3% and a sensitivity of 88.4%. In addition, the study found that the mean age at ACL rupture was 26.6 years, with a median return-to-play (RTP) time of 225 days. This is consistent with UEFA Elite Club Injury Study findings.
Outcomes and Implications
This highlights the potential of AI-driven tools in enhancing the accuracy and efficiency of sports medicine research. By reducing the need for time-consuming manual verification, the framework improves data reliability for injury surveillance and trend analysis in elite football. Clinically, the findings support existing literature on ACL injury recovery timelines and age-related injury risks. The framework’s high specificity shows it could be implemented for real-time monitoring of sports injuries, aiding physicians and researchers in tracking injury trends. Future innovations could expand its application to other injury types.