Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study.

Background: Radiology reports convey critical medical information to health care providers and patients. Unfortunately, they are often difficult for patients to comprehend, causing confusion and anxiety, thereby limiting patient engagement in health care decision-making. Large language models (LLMs) like ChatGPT (OpenAI) can create simplified, patient-friendly report summaries to increase accessibility, albeit with errors.

Objective: We evaluated the accuracy and clarity of ChatGPT-generated summaries compared to original radiologist-assessed radiology reports, assessed patients' understanding and satisfaction with the summaries compared to the original reports, and compared the readability of the original reports and summaries using validated readability metrics.

Methods: We anonymized 30 radiology reports created by neuroradiologists at our institution (6 brain magnetic resonance imaging, 6 brain computed tomography, 6 head and neck computed tomography angiography, 6 neck computed tomography, and 6 spine computed tomography). These anonymized reports were processed by ChatGPT to produce patient-centric summaries. Four board-certified neuroradiologists evaluated the ChatGPT-generated summaries on quality and accuracy compared to the original reports, and 4 patient volunteers separately evaluated the reports and summaries on perceived understandability and satisfaction. Readability was assessed using word count and validated readability scales.

Results: After reading the summary, patient confidence in understanding (98%, 116/118 vs 26%, 31/118) and satisfaction regarding the level of jargon/terminology (91%, 107/118 vs 8%, 9/118) and time taken to understand the content (97%, 115/118 vs 23%, 27/118) substantially improved. Ninety-two percent (108/118) of responses indicated the summary clarified patients' questions about the report, and 98% (116/118) of responses indicated patients would use the summary if available, with 67% (79/118) of responses indicating they would want access to both the report and summary, while 26% (31/118) of responses indicated only wanting the summary. Eighty-three percent (100/120) of radiologist responses indicated the summary represented the original report "extremely well" or "very well," with only 5% (6/120) of responses indicating it did so "slightly well" or "not well at all." Five percent (6/120) of responses indicated there was missing relevant medical information in the summary, 12% (14/120) reported instances of overemphasis of nonsignificant findings, and 18% (22/120) reported instances of underemphasis of significant findings. No fabricated findings were identified. Overall, 83% (99/120) of responses indicated that the summary would definitely/probably not lead patients to incorrect conclusions about the original report, with 10% (12/120) of responses indicating the summaries may do so.

Conclusions: ChatGPT-generated summaries could significantly improve perceived comprehension and satisfaction while accurately reflecting most key information from original radiology reports. Instances of minor omissions and under-/overemphasis were noted in some summaries, underscoring the need for ongoing validation and oversight. Overall, these artificial intelligence-generated, patient-centric summaries hold promise for enhancing patient-centered communication in radiology.