Artificial intelligence (AI) shows immense potential in medicine and Chat generative pretrained transformer (ChatGPT) has been used for different purposes in the field. However, it may not match the complexity and nuance of certain medical scenarios. This study evaluates the accuracy of ChatGPT 3.5 and 4 in providing recommendations regarding the management of postprostatectomy urinary incontinence (PPUI), considering The Incontinence After Prostate Treatment: AUA/SUFU Guideline as the best practice benchmark.
A set of questions based on the AUA/SUFU Guideline was prepared. Queries included 10 conceptual questions and 10 case-based questions. All questions were open and entered into the ChatGPT with a recommendation to limit the answer to 200 words, for greater objectivity. Responses were graded as correct (1 point); partially correct (0.5 point), or incorrect (0 point). Performances of versions 3.5 and 4 of ChatGPT were analyzed overall and separately for the conceptual and the case-based questions.
ChatGPT 3.5 scored 11.5 out of 20 points (57.5% accuracy), while ChatGPT 4 scored 18 (90.0%; p = 0.031). In the conceptual questions, ChatGPT 3.5 provided accurate answers to six questions along with one partially correct response and three incorrect answers, with a final score of 6.5. In contrast, ChatGPT 4 provided correct answers to eight questions and partially correct answers to two questions, scoring 9.0. In the case-based questions, ChatGPT 3.5 scored 5.0, while ChatGPT 4 scored 9.0. The domains where ChatGPT performed worst were evaluation, treatment options, surgical complications, and special situations.
ChatGPT 4 demonstrated superior performance compared to ChatGPT 3.5 in providing recommendations for the management of PPUI, using the AUA/SUFU Guideline as a benchmark. Continuous monitoring is essential for evaluating the development and precision of AI-generated medical information.
Neurourology and urodynamics. 2024 Mar 07 [Epub ahead of print]
Vicktor B P Pinto, Matheus F de Azevedo, Marcelo L Wroclawski, Guilherme Gentile, Vinicius L M Jesus, Jose de Bessa Junior, William C Nahas, Carlos A R Sacomani, Jaspreet S Sandhu, Cristiano M Gomes
Division of Urology, University of Sao Paulo School of Medicine, Sao Paulo, Brazil., Division of Urology, ABC Medical School, Sao Paulo, Brazil., Department of Surgery, State University of Feira de Santana, Bahia, Brazil., Innovation and Information Technology Sector, AC Camargo Cancer Hospital, Sao Paulo, Brazil., Department of Surgery/Urology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.