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Artificial Intelligence in Exercise, Rehabilitation and Health Promotion: A Clinical Perspective

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Sports Medicine".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 1489

Special Issue Editors


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Guest Editor
Data Convergence Team, Office of Hospital Information, Seoul National University Bundang Hospital, 172, Dolma-ro 1-gil, Bundang-gu, Seongnam 13605, Republic of Korea
Interests: artificial intelligence; machine learning; exercise and physical activity; rehabilitation and sports medicine; clinical applications; chronic disease; wearable devices and applications

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Guest Editor
Department of Judo Therapy, Faculty of Health and Medical Sciences, Nippon Sport Science University, 1221-1, Kamoshida-cho, Aoba-ku, Yokohama 227-0033, Japan
Interests: sports medicine; rehabilitation; musculoskeletal health; injury prevention; clinical exercise applications

Special Issue Information

Dear Colleagues,

This Special Issue will explore the transformative role of artificial intelligence (AI), machine learning (ML), and data-driven approaches in exercise, rehabilitation, and health promotion from a clinical perspective. Research in these fields has traditionally emphasized improving physical performance and supporting rehabilitation and health. With the integration of AI and ML, the scope now extends more deeply into clinical medicine and public health, enabling precise diagnostics, individualized rehabilitation strategies, and preventive interventions at both patient and population levels.

We welcome submissions highlighting innovative methods, clinical applications, including AI-driven approaches to chronic disease prevention and management, clinical decision support for injury prediction and rehabilitation, AI-based exercise prescription and personalized training, and applications of wearables, mobile health, and patient-generated health data, as well as multimodal and explainable AI in sports and health research.

This Special Issue aims to provide a platform on which clinicians and AI researchers can collaborate across disciplines, fostering advances that connect scientific knowledge with practical solutions for patient care and population health.

Dr. Ki-Hyuk Lee
Prof. Dr. Koji Koyama
Guest Editors

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Keywords

  • artificial intelligence
  • machine learning and deep learning
  • large language models (LLMs)
  • explainable AI
  • exercise and physical activity
  • rehabilitation and sports medicine
  • clinical applications
  • chronic disease and preventive health
  • digital health and big data analytics

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Published Papers (2 papers)

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Research

13 pages, 651 KB  
Article
AI-Generated Exercise Prescriptions for At-Risk Populations: Safety and Feasibility of a Large Language Model Assessed by Expert Evaluation
by Minkyung Choi, Jaeyong Park, Myeounggon Lee, Jaewon Beom, Se Young Jung and Kihyuk Lee
J. Clin. Med. 2026, 15(6), 2457; https://doi.org/10.3390/jcm15062457 - 23 Mar 2026
Viewed by 550
Abstract
Background/Objectives: In exercise science and sports medicine, the potential use of large language models for generating personalized exercise programs is being explored. However, the practical applicability of AI-generated exercise prescriptions has not yet been sufficiently validated, particularly in complex clinical contexts. This study [...] Read more.
Background/Objectives: In exercise science and sports medicine, the potential use of large language models for generating personalized exercise programs is being explored. However, the practical applicability of AI-generated exercise prescriptions has not yet been sufficiently validated, particularly in complex clinical contexts. This study aimed to evaluate their practical utility under expert supervision. Methods: Exercise prescription outputs generated by a large language model (Gemini 2.5, Google LLC) were analyzed using clinical cases incorporating complex exercise-related considerations. Three levels of prompt structuring were applied. Experts evaluated the outputs using a structured rubric assessing safety, feasibility, guideline alignment, and personalization. Inter-expert agreement was assessed using intraclass correlation coefficients (ICC), and expert-specific internal consistency was evaluated using Cronbach’s alpha. Results: AI-generated exercise prescriptions demonstrated a certain level of structural completeness. However, inter-expert agreement was low (ICC (2,3) = 0.139), whereas expert-specific internal consistency was high (Cronbach’s alpha > 0.92). Prompt structuring from Stage 1 to Stage 2 was associated with improved mean scores in safety and guideline alignment. Additional structuring did not consistently yield further improvements. Conclusions: AI-generated exercise prescriptions may have practical potential as supportive decision-making tools when expert involvement is assumed. Nonetheless, expert judgments did not converge toward a single evaluative standard, reflecting the inherently expert-dependent nature of exercise prescription. Full article
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14 pages, 2552 KB  
Article
Effects of 8 Weeks of Neuromuscular and SAQ Training on Physical Performance in Youth Soccer Players
by Yu-Bin Lee, Kwang-Jin Lee, Se-Young Seon and Keun-Ok An
J. Clin. Med. 2026, 15(3), 1202; https://doi.org/10.3390/jcm15031202 - 3 Feb 2026
Viewed by 586
Abstract
Backgrounds/Objectives: Adolescent soccer players are exposed to elevated injury risk due to rapid musculoskeletal development and high physical demands. Neuromuscular training (NMT) and speed–agility–quickness (SAQ) training are widely used to enhance performance and reduce injury risk in youth athletes. While both approaches are [...] Read more.
Backgrounds/Objectives: Adolescent soccer players are exposed to elevated injury risk due to rapid musculoskeletal development and high physical demands. Neuromuscular training (NMT) and speed–agility–quickness (SAQ) training are widely used to enhance performance and reduce injury risk in youth athletes. While both approaches are effective, comparative evidence regarding their modality-specific performance adaptations remains limited. Furthermore, few studies have discussed how such performance data may inform evidence-based or data-driven training selection in youth sports contexts. Methods: Thirty-six male youth soccer players with at least three years of playing experience, affiliated with Team A in Gyeonggi-do and Team B in Chungcheongbuk-do, participated in the study (NMTG, n = 21; SAQG, n = 15). Participants completed either an NMT or SAQ training program for eight weeks. To objectively assess exercise performance, pre- and post-tests were conducted measuring dynamic balance, vertical jump, zigzag run, and carioca. Results: Findings revealed a significant main effect of time for lower limb power (p < 0.05), but no significant group × time interaction, indicating that both NMTG and SAQG improved significantly over the 8-week period. Conversely, significant interaction effects were found for agility (p < 0.001), with SAQG demonstrating superior enhancements compared to NMTG. Dynamic balance showed no significant time effect or interaction. Conclusions: While NMTG and SAQG are equally effective for enhancing lower limb power, SAQG provides modality-specific advantages for agility in youth soccer players. These results emphasize time-dependent adaptations for power and the distinct benefits of SAQG for multi-directional speed. These adaptation profiles offer a data-driven framework for optimizing training selection in youth athletes. Full article
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