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Artificial Intelligence-Prompted Explanations of Common Primary Care Diagnoses
Mafaz Kattih1, Max Bressler1, Logan R Smith1
1Morsani College of Medicine, University of South Florida, Tampa, FL.
Primer (Leawood, Kan.)
|November 21, 2024
Summary
AI-generated medical explanations can improve patient understanding. Claude 2 and ChatGPT showed better readability and understandability than other AI tools, though practical use requires more study.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Patient Education
Background:
- Artificial intelligence (AI) may offer clearer medical explanations than traditional sources.
- AI-generated content can enhance patient understanding and autonomy in healthcare decisions.
Purpose of the Study:
- To evaluate the readability and understandability of AI-generated patient education materials.
- To compare different AI models for their effectiveness in explaining common medical diagnoses.
Main Methods:
- ChatGPT 3.5, Google Bard, HuggingChat, and Claude 2 generated patient education paragraphs for seven common diagnoses.
- Readability was assessed using Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL).
- Understandability and actionability were evaluated using the Patient Education Materials Assessment Tool (PEMAT).
Main Results:
- Claude 2 achieved the highest FRE (67.0) and understandability (69%), with an FKGL of 7.4.
- ChatGPT scored FRE 58.5, FKGL 9.3, and 69% understandability.
- Google Bard and HuggingChat showed lower scores in readability and understandability compared to Claude 2 and ChatGPT.
Conclusions:
- Claude 2 and ChatGPT offer superior readability and understandability for AI-generated patient education.
- Further research is needed to explore the practical application and impact on patient outcomes.
- Limitations include the rapid evolution of AI models and the subjective nature of some assessment tools.
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