Cil, GokhanDogan, Kazim2024-05-192024-05-1920240724-49831433-8726https://doi.org10.1007/s00345-024-04847-zhttps://hdl.handle.net/20.500.12713/4712PurposeThe study aimed to assess the efficacy of OpenAI's advanced AI model, ChatGPT, in diagnosing urological conditions, focusing on kidney stones.Materials and methodsA set of 90 structured questions, compliant with EAU Guidelines 2023, was curated by seasoned urologists for this investigation. We evaluated ChatGPT's performance based on the accuracy and completeness of its responses to two types of questions [binary (true/false) and descriptive (multiple-choice)], stratified into difficulty levels: easy, moderate, and complex. Furthermore, we analyzed the model's learning and adaptability capacity by reassessing the initially incorrect responses after a 2 week interval.ResultsThe model demonstrated commendable accuracy, correctly answering 80% of binary questions (n:45) and 93.3% of descriptive questions (n:45). The model's performance showed no significant variation across different question difficulty levels, with p-values of 0.548 for accuracy and 0.417 for completeness, respectively. Upon reassessment of initially 12 incorrect responses (9 binary to 3 descriptive) after two weeks, ChatGPT's accuracy showed substantial improvement. The mean accuracy score significantly increased from 1.58 +/- 0.51 to 2.83 +/- 0.93 (p = 0.004), underlining the model's ability to learn and adapt over time.ConclusionThese findings highlight the potential of ChatGPT in urological diagnostics, but also underscore areas requiring enhancement, especially in the completeness of responses to complex queries. The study endorses AI's incorporation into healthcare, while advocating for prudence and professional supervision in its application.eninfo:eu-repo/semantics/openAccessArtificial IntelligenceChatgptUrologyKidney StonesArticle42138483582WOS:0011843190000012-s2.0-85187857388N/A10.1007/s00345-024-04847-zQ1