AI-Generated Exercise Prescriptions for At-Risk Populations: Safety and Feasibility of a Large Language Model Assessed by Expert Evaluation

TL;DR

This study explores whether AI can safely create exercise plans for at-risk people, finding that while AI shows promise, expert input is still crucial.

Contribution

The study evaluates the safety and feasibility of AI-generated exercise prescriptions under expert supervision for complex clinical cases.

Findings

AI-generated exercise prescriptions showed structural completeness but lacked consistent expert agreement.

Prompt structuring improved safety and guideline alignment scores but did not consistently enhance other aspects.

Expert internal consistency was high, but inter-expert agreement was low, highlighting the subjective nature of exercise prescriptions.

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 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.

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