ArticleWorld journal of urology2024
Physician vs. AI-generated messages in urology: evaluation of accuracy, completeness, and preference by patients and physicians.
Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Use of Commercially Available Large Language Models to Generate Information Leaflets on Post-Intensive Care Syndrome: Clinical Utility Assessment.JMIR formative research · 2026Article
- Article
- Transforming Urological Care With Artificial Intelligence Chatbots: From Digital Assistants to Clinical Partners.International neurourology journal · 2026Review
- Comparative analysis of artificial intelligence platforms in generating Post-Operative instructions for endoscopic transnasal skull base surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- What patients want from healthcare chatbots: insights from a mixed-methods study.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication.NPJ digital medicine · 2025Article
- [AI-enabled clinical decision support systems: challenges and opportunities].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- A systematic review of early evidence on generative AI for drafting responses to patient messages.npj health systems · 2025Article
- Optimizing AI-assisted communication in urology: potential and challenges.World journal of urology · 2025Article
- Comment on "Physician vs. AI-generated messages in urology: evaluation of accuracy, completeness, and preference by patients and physicians".World journal of urology · 2025Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the accuracy, comprehensiveness, empathetic tone, and patient preference for AI and urologist responses to patient messages concerning common BPH questions across phases of care.
methodsCross-sectional study evaluating responses to 20 BPH-related questions generated by 2 AI chatbots and 4 urologists in a simulated clinical messaging environment without direct patient interaction. Accuracy, completeness, and empathetic tone of responses assessed by experts using Likert scales, and preferences and perceptions of authorship (chatbot vs. human) rated by non-medical evaluators.
resultsFive non-medical volunteers independently evaluated, ranked, and inferred the source for 120 responses (n = 600 total). For volunteer evaluations, the mean (SD) score of chatbots, 3.0 (1.4) (moderately empathetic) was significantly higher than urologists, 2.1 (1.1) (slightly empathetic) (p < 0.001); mean (SD) and preference ranking for chatbots, 2.6 (1.6), was significantly higher than urologist ranking, 3.9 (1.6) (p < 0.001). Two subject matter experts (SMEs) independently evaluated 120 responses each (answers to 20 questions from 4 urologist and 2 chatbots, n = 240 total). For SME evaluations, mean (SD) accuracy score for chatbots was 4.5 (1.1) (nearly all correct) and not significantly different than urologists, 4.6 (1.2). The mean (SD) completeness score for chatbots was 2.4 (0.8) (comprehensive), significantly higher than urologists, 1.6 (0.6) (adequate) (p < 0.001).
conclusionAnswers to patient BPH messages generated by chatbots were evaluated by experts as equally accurate and more complete than urologist answers. Non-medical volunteers preferred chatbot-generated messages and considered them more empathetic compared to answers generated by urologists.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.