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Large language models (LLMs) generate accurate diabetes education materials, but actionability and safety need improvement. DeepSeek R1 showed superior comprehensibility and safety, suggesting LLMs as valuable auxiliary tools requiring further development.

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ChatGPTDeepSeekartificial intelligencehealth education materiallarge language modelspatients with diabetes

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Diabetes Management Education
  • Digital Health Interventions

Background:

  • Continuous health education is crucial for diabetes self-management.
  • Large language models (LLMs) show potential for generating diabetes educational content.
  • Existing LLM studies often lack patient-specific clinical profile tailoring.

Purpose of the Study:

  • To compare the performance of ChatGPT-4o, Doubao 1.5, and DeepSeek R1 in generating diabetes health education materials.
  • To evaluate LLM-generated content based on accuracy, comprehensibility, actionability, personalization, effectiveness, and safety.
  • To assess the suitability of LLMs for creating tailored diabetes educational resources for discharged patients.

Main Methods:

  • Ten de-identified diabetes patient medical records were used as input for three LLMs.
  • Each LLM generated health education materials based on the provided clinical data.
  • Experienced diabetes nursing experts evaluated the quality of the generated materials using predefined criteria.

Main Results:

  • All models achieved >70% comprehensibility, with DeepSeek R1 performing best (P < .01).
  • Actionability scores were below 70% for all models, with no significant differences (P > .01).
  • Accuracy scores were high (≥98%) across all models; DeepSeek R1 had the highest safety score, while Doubao 1.5 had the lowest (P < .01).

Conclusions:

  • LLMs like ChatGPT-4o, Doubao 1.5, and DeepSeek R1 can produce accurate and comprehensible diabetes education materials.
  • Significant concerns exist regarding the actionability and safety of current LLM-generated content.
  • LLMs should be utilized as supplementary tools in diabetes education, necessitating further refinement for personalized and actionable patient content.