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Diagnostics
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Artificial Intelligence in Sports Medicine: Diagnosis and Management
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Artificial Intelligence in Sports Medicine: Diagnosis and Management

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 942

Special Issue Editor

Prof. Dr. Anca Ionescu

E-Mail Website
Guest Editor
Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 020022 Bucharest, Romania
Interests: sports medicine; sports cardiology; exercise physiology; athlete health; performance optimization; cardiovascular safety; artificial intelligence; wearable monitoring; biomarker profiling; sports nutrition; recovery science; evidence-based protocols for prevention, diagnosis, and rehabilitation in elite athletes

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is accelerating clinical progress in sports medicine by improving diagnostic accuracy, streamlining decision-making, and personalizing athlete monitoring. This Special Issue highlights validated AI methodologies that enhance the early detection and management of conditions associated with athletic performance, health, and safety.

Submissions are invited on AI-enabled applications regarding the following topics:

  • Sports cardiology and screening: automated ECG interpretation, arrhythmia and cardiomyopathy detection, AI-assisted echocardiography and CPET analytics, and prediction models for sudden cardiac death risk in athletes.
  • Musculoskeletal diagnostics and recovery optimization: computer vision and multimodal algorithms for accurate detection of soft-tissue and bone pathology, prognosis of reinjury risk, and objective return-to-play assessment.
  • Performance physiology and load regulation: sensor-based and wearable data analytics for quantifying neuromuscular function, cumulative training stress, biomechanical efficiency, and fatigue-related impairment.
  • Biomarker and molecular profiling: integration of biochemical, hormonal, and multi-omics signatures to identify maladaptation, overtraining, RED-S, and performance-limiting physiological dysregulation.
  • Validation and clinical governance: evidence-based evaluation of AI tools to confirm diagnostic accuracy, minimize error rates, ensure transparent decision-making, and clearly define clinician oversight when used in athlete care.

This Special Issue aims to advance safe, scientifically validated AI implementation that complements medical expertise and supports athlete well-being throughout the continuum of training, competition, and recovery.

Prof. Dr. Anca Ionescu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sports medicine
  • sports cardiology
  • exercise physiology
  • artificial intelligence
  • athlete health
  • performance monitoring
  • biomarkers
  • sports nutrition
  • injury prevention
  • rehabilitation
  • wearable technology
  • clinical validation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Review

26 pages, 1813 KB  
Review
Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician
by Stefano Palermi, Rita Pucciatti, Nor-Eddine Regnard, Ali Guermazi, Fabiano Araujo, Andrea Demeco, Yosra Mekki, Giuseppe D’Antona, Alessia Guarnera, Simone Cerciello, Matteo Guzzini and Marco Vecchiato
Diagnostics 2026, 16(10), 1448; https://doi.org/10.3390/diagnostics16101448 - 9 May 2026
Viewed by 539
Abstract
Background: Artificial intelligence (AI) is rapidly transforming healthcare, with increasing applications in sports medicine. Advances in machine learning, deep learning, and computer vision enable the analysis of large, heterogeneous datasets derived from imaging, wearable sensors, performance-monitoring systems, and electronic health records. While these [...] Read more.
Background: Artificial intelligence (AI) is rapidly transforming healthcare, with increasing applications in sports medicine. Advances in machine learning, deep learning, and computer vision enable the analysis of large, heterogeneous datasets derived from imaging, wearable sensors, performance-monitoring systems, and electronic health records. While these technologies offer opportunities to enhance injury prevention, diagnostic accuracy, rehabilitation monitoring, and clinical decision-making, their integration into athlete care remains complex and context-dependent. Methods: A structured narrative review of the PubMed/MEDLINE database was conducted to identify clinically relevant AI applications in sports medicine. The search focused on key domains including injury risk prediction, musculoskeletal imaging, rehabilitation monitoring, return-to-play assessment, performance management, and clinical workflow support. Evidence from original studies, reviews, methodological reports, and regulatory documents was qualitatively synthesized to provide an overview of current applications, methodological limitations, and decision-level implications. Results: AI demonstrates growing utility across multiple domains of sports medicine. Machine learning models can identify complex, non-linear relationships among training load, physiological responses, and injury risk, though their predictive performance varies widely and is often limited by dataset heterogeneity and a lack of external validation. In musculoskeletal imaging, AI-based algorithms support automated detection and quantification of abnormalities, with performance in selected tasks approaching that of expert readers, yet remaining task-specific and context-dependent. Emerging applications include movement analysis and rehabilitation monitoring through wearable sensors and computer vision systems, as well as data-driven support for return-to-play decisions and clinical workflow optimization. However, current evidence highlights important limitations, including algorithmic bias, limited generalizability, poor interpretability, and the risk of misapplication in complex clinical decision-making contexts. Conclusions: AI is likely to become an important decision-support layer in sports medicine by enabling data integration and longitudinal monitoring. However, model performance does not necessarily translate into improved clinical outcomes, and AI-generated predictions remain probabilistic and context-sensitive. Consequently, clinical decisions—particularly high-stakes processes such as return-to-play—require structured integration of AI outputs within a broader clinical framework. The sports physician remains central as a human-in-the-loop integrator, responsible for contextualizing AI-derived information, mitigating potential errors, and ensuring safe, individualized athlete management. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sports Medicine: Diagnosis and Management)
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