Evaluation of prostate cancer pathology reports generated by ChatGPT to enhance patient comprehension
The technical language of medical reports can be difficult for laypeople to understand. Artificial intelligence (AI) tools such as OpenAI's ChatGPT present an opportunity to bridge this gap. This quality improvement initiative explored ChatGPT's potential to translate traditional prostate cancer pathology reports into patient-friendly versions. Using institutional templates, we created 25 prostate biopsy and 25 radical prostatectomy reports and converted them into patient-centered versions using ChatGPT-3.5. Nine urologists evaluated these reports. Additionally, 41 patients participating in the initiative were asked to provide feedback on their understanding of the biopsy reports. Feedback was collected using structured questionnaires and readability scores. The AI-generated biopsy and surgical pathology reports had higher mean Flesch Reading Ease Scores (49 ± 5; 50 ± 3) compared to the original reports (31 ± 15, p < 0.001; 41 ± 17, p = 0.02, respectively). Surgeons expressed positive opinions about AI-generated biopsy and surgical pathology reports, including patient-friendly language (77% and 95%, respectively). Patients also agreed that the AI-generated biopsy reports were well-structured and easy to follow (90%) and helpful in understanding their diagnosis (88%). 76% of patients preferred AI-generated reports as part of their care. These findings suggest that AI-developed reports may improve readability and support patient understanding.
Authors
- Antônio Rebello Horta Görgen
- Tuan Thanh Nguyen (ORCID: https://orcid.org/0000-0002-4309-8082)
- David I. Lee (ORCID: https://orcid.org/0000-0003-2133-9284)
- Narmina Khanmammadova (ORCID: https://orcid.org/0000-0001-8637-8319)
- Jaime Altamirano‐Villarroel (ORCID: https://orcid.org/0000-0001-8529-8552)
- Michael Daneshvar (ORCID: https://orcid.org/0000-0002-0670-1110)
- Sohrab N. Ali (ORCID: https://orcid.org/0000-0002-9557-4837)
- Rafael Gevorkyan
- Daniel Jiang (ORCID: https://orcid.org/0000-0002-5388-8061)
- Andrei D. Cumpanas
- Jacob Basilius
- Mohammed Shahait (ORCID: https://orcid.org/0000-0003-2609-5629)
- Thomas E. Ahlering (ORCID: https://orcid.org/0000-0002-6492-4910)
- Kristene Myklak
- Andrew S. Afyouni
Institutions
- Intermountain Healthcare (US)
- University of Southern California (US)
- City Of Hope National Medical Center (US)
- University of California, Irvine (US)
- University of Medicine and Pharmacy at Ho Chi Minh City (VN)
- Intermountain Medical Center (US)
- Hospital de Clínicas de Porto Alegre (BR)
- City of Hope (US)
- Fundación Arturo López Pérez (CL)
- California State University, Fresno (US)
Publication Details
- Journal
- Scientific Reports
- Published
- 2025-10-02
- DOI
- https://doi.org/10.1038/s41598-025-17639-1
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65