Performance of Retrieval-Augmented Generation Large Language Models in Guideline-Concordant Prostate-Specific Antigen Testing: Comparative Study With Junior Clinicians
Use of RAG techniques allows LLMs to integrate complex guidelines into day-to-day medical decision-making. RAG-LLM tools in urology have the capability to enhance clinical decision-making by providing guideline-concordant recommendations for PSA testing, potentially improving the consistency of health care delivery, reducing cognitive load on clinicians, and reducing unnecessary investigations and costs. While this study used synthetic cases in a controlled simulation environment, it establishes a foundation for future validation in real-world clinical settings.
Authors
- Kae Jack Tay (ORCID: https://orcid.org/0000-0003-0365-1899)
- Quang A. Le (ORCID: https://orcid.org/0000-0002-4968-9334)
- Kenneth Chen (ORCID: https://orcid.org/0000-0002-3844-7567)
- Yifei Huang (ORCID: https://orcid.org/0000-0002-5382-966X)
- Gerald Gui Ren Sng (ORCID: https://orcid.org/0000-0002-8795-7491)
- Daniel Yan Zheng Lim (ORCID: https://orcid.org/0000-0002-9715-6970)
- John Shyi Peng Yuen (ORCID: https://orcid.org/0000-0002-6367-2563)
- Joshua Yi Min Tung (ORCID: https://orcid.org/0000-0003-4991-9790)
- Yu Guang Tan (ORCID: https://orcid.org/0000-0002-8166-0582)
- Jen Hong Tan (ORCID: https://orcid.org/0000-0002-7785-2987)
- Raymond Y.K. Lau (ORCID: https://orcid.org/0000-0002-5751-4550)
- Jinxuan Yao
- Christopher Wai Sam Cheng (ORCID: https://orcid.org/0000-0002-5274-3004)
- Henry Sun Sien Ho
Institutions
- Singapore General Hospital (SG)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-10-12
- DOI
- https://doi.org/10.2196/78393
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 4.96