The efficacy of artificial intelligence in urology: a detailed analysis of kidney stone-related queries

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dc.authoridCil, Gokhan/0000-0001-8997-3164
dc.contributor.authorCil, Gokhan
dc.contributor.authorDogan, Kazim
dc.date.accessioned2024-05-19T14:39:09Z
dc.date.available2024-05-19T14:39:09Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractPurposeThe 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.en_US
dc.description.sponsorshipUniversity of Health Sciencesen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1007/s00345-024-04847-z
dc.identifier.issn0724-4983
dc.identifier.issn1433-8726
dc.identifier.issue1en_US
dc.identifier.pmid38483582en_US
dc.identifier.scopus2-s2.0-85187857388en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s00345-024-04847-z
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4712
dc.identifier.volume42en_US
dc.identifier.wosWOS:001184319000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofWorld Journal of Urologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectChatgpten_US
dc.subjectUrologyen_US
dc.subjectKidney Stonesen_US
dc.titleThe efficacy of artificial intelligence in urology: a detailed analysis of kidney stone-related queriesen_US
dc.typeArticleen_US

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