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Mapping Artificial Intelligence and Machine Learning Research in Sports Injury Prediction: A Bibliometric Analysis
Conference paper

Mapping Artificial Intelligence and Machine Learning Research in Sports Injury Prediction: A Bibliometric Analysis

Mostafa Kamalpour, Rania Shibl, Mansoureh Mirzaei, Shazi Saremi, Anthony Bedford and Erica Mealy
pp.1-10
Australasian Conference on Mathematics and Computers in Sport (ANZIAM): MathSport, 18th (Gold Coast, Australia, 01-Jul-2026–03-Jul-2026)
Australian and New Zealand Industrial and Applied Mathematics
2026

Abstract

Artificial intelligence machine learning sports injury bibliometrics
Recent advances in artificial intelligence (AI) and machine learning (ML) have significantly enhanced sports injury prediction and prevention. However, the rapid growth of this field has led to a fragmented understanding of its intellectual structure, collaboration patterns, and emerging trends. This study addresses this gap through a bibliometric analysis of 3,066 publications from the Web of Science Core Collection (1990–2026), using VOSviewer to examine publication trends, leading contributors, collaboration networks, and thematic development. The findings indicate rapid expansion since 2015, increasing global collaboration, and a concentration of research output among leading countries, including the US, China, and the UK. Thematic analysis reveals a shift toward generalisable, data-driven models, with machine learning as the central methodological driver. This study contributes by providing a structured mapping of the field and informing future research and policy development.

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