Patient-facing diabetic foot information from large language models: a domain- and source-balanced prompt framework for public-interface benchmarking - Takeaways - MDSpire

Evaluating Patient-Centric Diabetic Foot Information Generated by Large Language Models: A Framework for Balanced Prompt Design and Public Interface Assessment

  • By

  • Yang Wen

  • Liyuan Chen

  • Jiaping Lan

  • Lin Chen

  • Lei Li

  • July 21, 2026

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  • 1

    The study developed a balanced prompt framework for benchmarking diabetic foot information generated by large language models.

  • 2

    Responses from five LLMs were evaluated for quality, transparency, readability, and potential clinical risk signals.

  • 3

    Grok 4.3 achieved the highest scores in quality metrics, while DeepSeek-V4 had the lowest readability scores.

  • 4

    No responses met all predefined readability targets, indicating a need for improvement in patient-facing information.

  • 5

    The findings suggest that LLM-generated diabetic foot information should not be relied upon without clinician oversight.

Original Source(s)

Patient-facing diabetic foot information from large language models: a domain- and source-balanced prompt framework for public-interface benchmarking

  • by Yang Wen, Liyuan Chen, Jiaping Lan, Lin Chen, Lei Li

  • July 21, 2026

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