Improving Patient Understanding of Glomerular Disease Terms With ChatGPT

Background: Glomerular disease is complex and difficult for patients to understand, as it involves various pathophysiology, immunology, and pharmacology areas. Objective: This study explored whether ChatGPT can maintain accuracy while simplifying glomerular disease terms to enhance patient comprehension. Methods: 67 terms related to glomerular disease were analyzed using GPT-4 through two distinct queries. One aimed at a general explanation and another tailored for patients with an education level of 8th grade or lower. GPT-4's accuracy was scored from 1 (incorrect) to 5 (correct and comprehensive). Its readability was assessed using the Consensus Reading Grade (CRG) Level, which incorporates seven readability indices including the Flesch-Kincaid Grade (FKG) and SMOG indices. Flesch Reading Ease (FRE) score, ranging from 0 to 100 with higher scores indicating easier-to-read text, was also used to evaluate the readability. A paired t-test was conducted to assess differences in accuracy and readability levels between different queries. Results: GPT-4's general explanations of glomerular disease terms averaged at a college readability level, indicated by the CRG score of 14.1 and FKG score of 13.9. SMOG index also indicated the topic's complexity, with a score of 11.8. When tailored for patients at or below an 8(th)-grade reading level, readability improved, averaging 9.7 by the CRG score, 8.7 by FKG score, and 7.3 by SMOG score. The FRE score also indicated a further improvement of readability from 31.6 for general explanations to 63.5 for tailored explanations. However, the accuracy in GPT-4's tailored explanations was significantly lower than that in general explanations (4.2 +/- 0.4 versus 4.7 +/- 0.3, p < 0.0001). Conclusion: While GPT-4 effectively simplified information about glomerular diseases, it compromised its accuracy in the process. To implement these findings, we suggest pilot studies in clinical settings to assess patient understanding, using feedback from diverse groups to customize content, expanding research to enhance AI accuracy and reduce biases, setting strict ethical guidelines for AI in healthcare, and integrating with health informatics systems to provide tailored educational content to patients. This approach will promote effective and ethical use of AI tools like ChatGPT in patient education, empowering patients to make informed health decisions.