Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Quality of information and appropriateness of ChatGPT outputs for urology patients



The proportion of health-related searches on the internet is continuously growing. ChatGPT, a natural language processing (NLP) tool created by OpenAI, has been gaining increasing user attention and can potentially be used as a source for obtaining information related to health concerns. This study aims to analyze the quality and appropriateness of ChatGPT’s responses to Urology case studies compared to those of a urologist.


Data from 100 patient case studies, comprising patient demographics, medical history, and urologic complaints, were sequentially inputted into ChatGPT, one by one. A question was posed to determine the most likely diagnosis, suggested examinations, and treatment options. The responses generated by ChatGPT were then compared to those provided by a board-certified urologist who was blinded to ChatGPT’s responses and graded on a 5-point Likert scale based on accuracy, comprehensiveness, and clarity as criterias for appropriateness. The quality of information was graded based on the section 2 of the DISCERN tool and readability assessments were performed using the Flesch Reading Ease (FRE) and Flesch-Kincaid Reading Grade Level (FKGL) formulas.


52% of all responses were deemed appropriate. ChatGPT provided more appropriate responses for non-oncology conditions (58.5%) compared to oncology (52.6%) and emergency urology cases (11.1%) (p = 0.03). The median score of the DISCERN tool was 15 (IQR = 5.3) corresponding to a quality score of poor. The ChatGPT responses demonstrated a college graduate reading level, as indicated by the median FRE score of 18 (IQR = 21) and the median FKGL score of 15.8 (IQR = 3).


ChatGPT serves as an interactive tool for providing medical information online, offering the possibility of enhancing health outcomes and patient satisfaction. Nevertheless, the insufficient appropriateness and poor quality of the responses on Urology cases emphasizes the importance of thorough evaluation and use of NLP-generated outputs when addressing health-related concerns.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.


  1. Wise J. How Many People Use the Internet Daily in 2023? - EarthWeb. (accessed 15 May2023).

  2. NTIA. More than Half of American Households Used the Internet for Health-Related Activities in 2019, NTIA Data Show | National Telecommunications and Information Administration. (accessed 2 May2023).

  3. Eysenbach G, Kohler C. What is the prevalence of health-related searches on the World Wide Web? Qualitative and quantitative analysis of search engine queries on the Internet. AMIA Annu Symp Proc. 2003;2003:225.

    PubMed  PubMed Central  Google Scholar 

  4. Introducing ChatGPT. (accessed 15 May2023).

  5. Liu Y, Yang Z, Yu Z, Liu Z, Liu D, Lin H, et al. Generative artificial intelligence and its applications in materials science: Current situation and future perspectives. J Mater. 2023.

  6. Haleem A, Javaid M, Singh RP. An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Trans Benchmarks, Stand Eval. 2022;2:100089.

    Article  Google Scholar 

  7. Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med. 2023.

  8. Homolak J. Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma. Croat Med J. 2023;64:1.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Charnock D, Shepperd S, Needham G, Gann R. DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. J Epidemiol Community Health. 1999;53:105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Daraz L, Morrow AS, Ponce OJ, Beuschel B, Farah MH, Katabi A, et al. Can Patients Trust Online Health Information? A Meta-narrative Systematic Review Addressing the Quality of Health Information on the Internet. J Gen Intern Med. 2019;34:1884–91.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sbaffi L, Rowley J. Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research. J Med Internet Res. 2017;19.

  12. Zhou Z, Wang X, Li X, Liao L. Is ChatGPT an Evidence-based Doctor? Eur Urol. 2023.

  13. Van Bulck L, Moons P. What if your patient switches from Dr. Google to Dr. ChatGPT? A vignette-based survey of the trustworthiness, value and danger of ChatGPT-generated responses to health questions. Eur J Cardiovasc Nurs. 2023.

  14. Sallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthc (Basel, Switzerland) 2023;11.

  15. Checcucci E, Rosati S, De Cillis S, Vagni M, Giordano N, Piana A, et al. Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic. Prostate Cancer Prostatic Dis. 2021;25:359–62.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lombardo R, De Nunzio C. Nomograms in PCa: where do we stand. Prostate Cancer Prostatic Dis. 2023;10. Online ahead of print.

Download references

Author information

Authors and Affiliations



AC: Protocol/project development, Data collection and management, Data analysis, Manuscript writing/editing. MP: Data collection and management, MLR: Data collection and management. GIR: Manuscript writing/editing. MGA: Manuscript writing/editing. MF: Manuscript writing/editing. GC: Data analysis, Manuscript writing/editing. SC: Manuscript writing/editing. AM: Data collection and management, Manuscript writing/editing. ED: Protocol/project development, Data collection and management, Data analysis, Manuscript writing/editing.

Corresponding author

Correspondence to Andrea Cocci.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The data was treated anonymously, and the local ethical approval was not required. The study was performed in accordance with the Declaration of Helsinki.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cocci, A., Pezzoli, M., Lo Re, M. et al. Quality of information and appropriateness of ChatGPT outputs for urology patients. Prostate Cancer Prostatic Dis (2023).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI:

This article is cited by


Quick links