ArticleBiology of sport2024
Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model.
Article in Biology of sport, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 92 citations in OpenAlex.
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
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- Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence.Sports medicine (Auckland, N.Z.) · 2026Review
- Digital Decisions: Enhancing Chronic Disease Self-Care Through Digital Health and AI-Enhanced Decision-Making.Journal of medical Internet research · 2026Article
- Science Is About Thinking: How Can We Protect Thinking Time in a Distracted Digital World?Brain sciences · 2026Review
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- Integrating Biological Maturity into Fitness Assessment and Physical Activity Interventions in Children and Adolescents: A Narrative Review.Sports (Basel, Switzerland) · 2026Review
- Large Language Model-Based Agents for Physical Activity and Cognitive Training: Scoping Review.JMIR AI · 2026Review
- The Role of Artificial Intelligence and Professional Expertise in Adapted Physical Activity Prescription for Orthopedic Rehabilitation.Journal of functional morphology and kinesiology · 2026Article
- ChatGPT Outperforms Personal Trainers in Answering Common Exercise Training Questions.Journal of sports science & medicine · 2026Article
- Clinical Validation of an On-Device AI-Driven Real-Time Human Pose Estimation and Exercise Prescription Program; Prospective Single-Arm Quasi-Experimental Study.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.Biology of sport · 2026Article
- More details, less variability? A crossover design study on the impact of information granularity on ChatGPT's training program stability.Biology of sport · 2026Article
- Toward autonomous artificial intelligence agents in sports science: a modular framework for development, validation, and implementation.Biology of sport · 2026Review
- Physiological relevance and generation-level stability of artificial intelligence-generated race-day warm-up and cool-down plans for sprint swimming: an expert-rated comparison of four large language models.Frontiers in physiology · 2026Article
- The AI recommendation paradox: a systematic review evaluating the promise, peril, and path forward for large language models in exercise recommendation.Biology of sport · 2026Review
- Article
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
Corrections and comments
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Authors and funding
32 authors at 20 institutions in 24 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.