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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

Comparative performance of four large language models in generating evidence-based exercise prescriptions using

Huan Feng1, Xiaojun Wang1

  • 1College of Physical Education, Sichuan University, Chengdu, China.

Frontiers in Physiology
|June 10, 2026
PubMed
Summary

Claude 3.7 demonstrated superior performance in generating exercise prescriptions using the FITT-VP framework compared to other large language models (LLMs). This AI shows promise for personalized exercise medicine when used collaboratively with healthcare professionals.

Keywords:
DeepSeek R1FITT-VP frameworkGPT-4oartificial intelligenceclaude 3.7clinical decision supportcomparative evaluationexercise prescription

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Area of Science:

  • Artificial Intelligence in Medicine
  • Exercise Science
  • Digital Health

Background:

  • Exercise prescription is vital for health management but faces limitations due to practitioner expertise and time constraints.
  • Large language models (LLMs) present a potential solution for personalized exercise prescriptions, but their comparative effectiveness is not well-established.

Purpose of the Study:

  • To evaluate and compare the performance of four advanced LLMs in generating exercise prescriptions.
  • To assess LLM-generated prescriptions using the established FITT-VP framework.

Main Methods:

  • Four LLMs (GPT-4o, Claude 3.7, DeepSeek R1, Grok-3) were tested.
  • Thirty synthetic patient profiles were created based on epidemiological data and clinical guidelines.
  • Certified exercise specialists rated generated prescriptions on FITT-VP dimensions (Frequency, Intensity, Time, Type, Volume, Progression).

Main Results:

  • Claude 3.7 achieved the highest overall score (50.23 ± 1.75) out of a possible 60.
  • Significant differences were found among LLMs (p < 0.001), with Claude 3.7 outperforming GPT-4o and DeepSeek R1.
  • Claude 3.7 excelled in Time and Progression, while DeepSeek R1 performed poorly in Intensity and Type.

Conclusions:

  • Claude 3.7 shows potential as a drafting tool for personalized exercise prescriptions within a human-AI collaborative framework.
  • Further research is needed to assess multi-run variability and clinical validation for real-world application.