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@article{205310,
        author = {Anil Kumar},
        title = {Artificial Intelligence in Sports Injury Prediction, Diagnosis, and Rehabilitation: An Integrative Review of Multimodal Approaches, Risk Factor Identification, and Clinical Implications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6357-6364},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205310},
        abstract = {Sports injuries represent a significant global health burden affecting athletes at all levels of competition, with profound implications for performance, career longevity, psychological wellbeing, and healthcare costs. Traditional approaches to sports injury management relying primarily on manual clinical assessment, subjective observation, and standardized intervention protocols are inherently limited in their accuracy, scalability, and capacity for personalization. This integrative review synthesizes findings from three contemporary publications to comprehensively examine the current state, methodological diversity, and clinical implications of artificial intelligence (AI) applied to sports injury prediction, diagnosis, and rehabilitation. The review evaluates both classical machine learning and advanced deep learning frameworks, multimodal sensor integration, explainable AI techniques, and real-world deployment strategies. Three source documents were reviewed: (1) an original research article proposing the Biomechanically Informed Neural Network (BINN) and Adaptive Sports Medicine Strategy (ASMS) frameworks; (2) a systematic review of multimodal AI technologies for sports injury prediction and rehabilitation; and (3) a scoping review mapping AI -based identification of conventional and unconventional risk factors for sports injuries across 59 studies. Findings were synthesized narratively across key themes including data modalities, algorithmic approaches, clinical outcomes, and deployment challenges. AI driven frameworks demonstrate consistent superiority over traditional approaches across multiple performance metrics. The proposed BINN+ASMS model achieved an AUC of 0.912 on musculoskeletal radiograph datasets and 0.902 on sports specific injury data, with strong clinical correlation scores confirmed by domain experts. Across 59 studies in the scoping review, tree-based ensemble methods (25.4%) and support vector machines (15.3%) dominated the landscape, with deep learning approaches increasingly applied to temporal and image-based data. Multi modal sensor integration combining kinematic, physiological, and contextual data emerged as a consistent predictor of improved model performance. Unconventional risk factors including sleep quality, psychological stress, and accumulated fatigue were increasingly incorporated alongside conventional biomechanical predictors. AI technologies hold transformative potential for sports medicine through enhanced injury risk prediction, personalized rehabilitation, and real-time monitoring. However, significant challenges remain in model generalizability, external validation, interpretability, data privacy, and clinical integration. Future progress will require multicenter prospective studies, standardized reporting frameworks, and deliberate translation of AI outputs into athlete care pathways.},
        keywords = {Artificial Intelligence, Sports Injury, Biomechanical Analysis, Machine Learning, Deep Learning, Wearable Sensors, Multimodal Data Fusion, Rehabilitation Optimization, Explainable AI, Injury Risk Prediction, BINN, ASMS, Edge Computing, Personalized Medicine.},
        month = {June},
        }