FP8.6 Machine Learning and Artificial Intelligence in High-Impact Sports Medicine Journals: A Scientometric Mapping and Methodologic Quality Audit

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ID: 331375
2026
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Abstract
Abstract ICLA08 - AI and 3D Simulations: Trustworthy Clinical Partners or Tech Hype? Auditorium B (BMCC Level 1), October 8, 2026, 14:00 - 16:00 Background Machine Learning (ML) and Artificial Intelligence (AI) are rapidly reshaping sports medicine, with expanding applications in injury prediction, imaging diagnostics, and clinical outcome modeling. However, the intellectual structure of this field and the clinical readiness of published models remain poorly defined. This study aimed to map research trends and audit the methodologic rigor of AI research in high impact sports medicine journals. Methods A scientometric analysis and methodological quality audit were conducted across the top five core sports medicine journals (BJSM, Sports Medicine, AJSM, Arthroscopy, and KSSTA) from 2010 to 2025. Bibliometric metadata were analyzed using the bibliometrix R-package and VOSviewer. Methodological quality and reporting completeness were evaluated using TRIPOD+AI, CLAIM, and PROBAST+AI checklists. Qualitative trends were assessed via descriptive statistics. Results The search strategy yielded 279 records, of which 210 met inclusion criteria for bibliometric mapping. A subset of 93 original research studies qualified for formal quality audit. Bibliometric analysis revealed an exponential growth in publications, peaking with 75 articles in 2025. KSSTA (n=83) and Arthroscopy (n=77) were the primary drivers of AI literature. The USA emerged as the central node in international collaboration, with the Hospital for Special Surgery identified as the most prolific institution. Keyword co-occurrence identified three primary clusters: (1) prognostic clinical prediction, (2) diagnostic imaging/deep learning, and (3) biomechanical classification. In the quality audit (n=93), reporting completeness was 79.9% for TRIPOD+AI and 89.6% for CLAIM. PROBAST+AI indicated that 24% of studies were at a high risk of bias, particularly in the "Analysis" domain. Indicators of clinical translation were limited: 51.6% of studies performed external validation, while only 35.5% reported calibration, 22.6% used prospective data, 21.5% defined clinical decision thresholds, and 7.5% provided open-source code. Conclusion AI research in sports medicine has transitioned from a niche interest to a primary research pillar, primarily focused on hip, knee, and shoulder pathology. However, a significant "translation gap" persists. Despite high rates of statistical validation, the lack of prospective evaluation, poor calibration reporting, and lack of model transparency constrain clinical application. Future research must prioritize transparency and clinical utility over raw algorithmic performance.
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Authors Navid Ghaderi, Bona Baissa, Robert Koucheki, Aazad Abbas, Kalter Hali, Robert DiCesare, Tyler Hauer
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Journal Journal of hip preservation surgery
Year 2026
DOI
10.1093/jhps/hnag043.062
URL
https://doi.org/10.1093/jhps/hnag043.062
Keywords Keywords not found

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