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Assessment of Large Language Models for Enhancing Diabetologist-Developed Personalized Treatment Plans in Complex
Michelle Wang1,2, Madhumita Sushil2, Christopher Y K Williams2
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA.
Summary
This study explored using GPT-4 for type 2 diabetes treatment plans. While GPT-4 identified goals well, it struggled with identifying areas for improvement, though its explanations offered value to physicians.
Area of Science:
- Artificial Intelligence in Medicine
- Endocrinology
- Diabetes Management
Background:
- Type 2 diabetes treatment is complex and requires personalized plans.
- Large language models (LLMs) show potential for clinical decision support.
Purpose of the Study:
- To evaluate the utility of GPT-4 in enhancing diabetes treatment plans.
- To assess GPT-4's ability to extract and critically evaluate treatment plans from clinical notes.
Main Methods:
- GPT-4 was used to analyze diabetologist-developed treatment plans from patient clinical notes.
- The model's performance in identifying personalized goals and areas for improvement was assessed.
- Experienced diabetologists reviewed GPT-4's reasoning and explanations.
Main Results:
- GPT-4 accurately identified personalized treatment goals for type 2 diabetes.
- The model performed suboptimally in pinpointing treatment plans needing further personalization.
- 40% of GPT-4's reasoning and explanations were deemed helpful by expert diabetologists.
Conclusions:
- GPT-4 shows promise in supporting diabetes treatment planning, particularly for identifying patient goals.
- While not perfect, GPT-4's insights can benefit less experienced physicians, such as primary care providers.
- Further development may enhance LLMs' capabilities in clinical decision support for complex conditions like diabetes.
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