{
  "abstract": "Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice.",
  "abstract_shown": true,
  "affiliations": [
    "Research Unit “Sport Sciences, Health and Movement”, High Institute of Sports and Physical Education of Kef, University of Jendouba, Kef 7100, Tunisia",
    "Training Department, Police College, Qatar Police Academy, Doha 7157, Qatar",
    "Faculty of Sports Sciences, Atatürk University, Erzurum 25240, Türkiye",
    "Heart Failure Research Laboratory (LR12SP09), Farhat HACHED Hospital, Faculty of Medicine of Sousse, University of Sousse, Sousse 4054, Tunisia",
    "Naufar Center, Doha P.O. Box 93097, Qatar",
    "Research Institute for Sport and Exercise, University of Canberra, Canberra, ACT 2601, Australia",
    "Division of Cardiology and Angiology, University Hospital Magdeburg, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany",
    "Institut Supérieur de Sport et de l’Education Physique du Kef, Université de Jendouba, Le Kef 7100, Tunisia"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wissem Dhahbi",
      "orcid": "0000-0001-6221-546X"
    },
    {
      "affs": [
        0
      ],
      "name": "Nidhal Jebabli",
      "orcid": "0000-0002-6328-4915"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Marouen Souaifi",
      "orcid": "0009-0002-3387-9886"
    },
    {
      "affs": [
        2
      ],
      "name": "Halil İ̇brahim Ceylan",
      "orcid": "0000-0003-1133-5511"
    },
    {
      "affs": [
        3
      ],
      "name": "Helmi Ben Saad",
      "orcid": "0000-0002-7477-2965"
    },
    {
      "affs": [
        4
      ],
      "name": "Karim Chamari",
      "orcid": "0000-0001-9178-7678"
    },
    {
      "affs": [
        5
      ],
      "name": "David B. Pyne",
      "orcid": "0000-0003-1555-5079"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Hélmi Chaabène",
      "orcid": "0000-0001-7812-7931"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ahrzxm13zthhm.bib",
  "cited_by": 27,
  "doi": "10.3390/bioengineering13060692",
  "fulltext_url": null,
  "id": "ahrzxm13zthhm",
  "issns": [
    "2306-5354"
  ],
  "landing_url": "https://doi.org/10.3390/bioengineering13060692",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "2732|Sports injuries and prevention"
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  "pdf_url": "https://www.mdpi.com/2306-5354/13/6/692/pdf?version=1781691330",
  "pmcid": "PMC13296066",
  "pmid": "42351936",
  "published": "2026-06-17",
  "publisher": "Multidisciplinary Digital Publishing Institute",
  "ris": "https://research.draconitic.com/paper/ahrzxm13zthhm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:54Z",
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      "url": "https://doi.org/10.3390/bioengineering13060692"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2732|Sports injuries and prevention",
      "subfield": "surgery"
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  "title": "Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review",
  "title_text": "Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:15Z",
  "url": "https://research.draconitic.com/paper/ahrzxm13zthhm",
  "venue": "Bioengineering",
  "versions": []
}