Artificial Intelligence-Prompted Explanations of Common Primary Care Diagnoses.

Background: Artificial intelligence (AI)-generated explanations about medical topics may be clearer and more accessible than traditional evidence-based sources, enhancing patient understanding and autonomy. We evaluated different AI explanations for patients about common diagnoses to aid in patient care.

Methods: We prompted ChatGPT 3.5, Google Bard, HuggingChat, and Claude 2 separately to generate a short patient education paragraph about seven common diagnoses. We used the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) to evaluate the readability and grade level of the responses. We used the Agency for Healthcare Research and Quality's Patient Education Materials Assessment Tool (PEMAT) grading rubric to evaluate the understandability and actionability of responses.

Results: Claude 2 demonstrated scores of FRE (67.0), FKGL (7.4), and PEMAT, 69% for understandability, and 34% for actionability. ChatGPT scores were FRE (58.5), FKGL (9.3), PEMAT (69% and 31%, respectively). Google Bard scores were FRE (50.1), FKGL (9.9), PEMAT (52% and 23%). HuggingChat scores were FRE (48.7) and FKGL (11.6), PEMAT (57% and 29%).

Conclusion: Claude 2 and ChatGPT demonstrated superior readability and understandability, but practical application and patient outcomes need further exploration. This study is limited by the rapid development of these tools with newer improved models replacing the older ones. Additionally, the accuracy and clarity of AI responses is based on that of the user-generated response. The PEMAT grading rubric is also mainly used for patient information leaflets that include visual aids and may contain subjective evaluations.