ArticleEuropean urology open science2024
Comparing Patient's Confidence in Clinical Capabilities in Urology: Large Language Models Versus Urologists.
Article in European urology open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues.Journal of medical Internet research · 2025Pooled it
- [Large language models as a communication and organizational infrastructure in urology: evidence, limitations, and clinical responsibility].Urologie (Heidelberg, Germany) · 2026Review
- Readability of Chatbot Responses in Prostate Cancer and Urological Care: Objective Metrics Versus Patient Perceptions.Current oncology (Toronto, Ont.) · 2025Article
- [Digital urology : Possible uses for artificial intelligence and digital health applications].Urologie (Heidelberg, Germany) · 2025Review
- Patient insights into empathy, compassion and self-disclosure in medical large language models: results from the IPALLM III study.World journal of urology · 2025Article
- Evaluating interactions of patients with large language models for medical information.BJU international · 2025Article
- A GPT-reinforced social robot for patient communication: a pilot study.Frontiers in digital health · 2025Article
- Generative AI in urology: rethinking patient counselling and shared decision-making - a scoping review from the European Association of Urology Patient Office.Therapeutic advances in urologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: Data on interaction of patients with artificial intelligence (AI) are limited, primarily derived from small-scale studies, cross-sectional surveys, and qualitative reviews. Most patients have not yet encountered AI in their clinical experience. This study explored patients' confidence in AI, specifically large language models, after a direct interaction with a chatbot in a clinical setting. Through hands-on experience, the study sought to reduce potential biases due to an anticipated lack of AI experience in a real-world urological patient sample. Methods: A total of 300 patients scheduled for counseling were enrolled from February to July 2024. Participants voluntarily conversed about their medical questions with a GPT-4 powered chatbot, followed by a survey assessing their confidence in clinical capabilities of AI compared with their counseling urologists. Clinical capabilities included history taking, diagnostics, treatment recommendation, anxiety reduction, and time allocation. Key findings and limitations: Of the 292 patients who completed the study, AI was significantly preferred to physicians for consultation time allocation ( Conclusions and clinical implications: Our study demonstrates that urological patients prefer AI as a powerful complement to-rather than a replacement for-human expertise in clinical care. Patients appreciated the additional consultation time provided by AI. Interestingly, age was not associated with confidence in AI, suggesting that large language models are user-friendly tools for patients of all age groups. Patient summary: In this report, we explored how patients feel about using an artificial intelligence (AI)-powered chatbot in a medical setting. Patients interacted with the AI for medical questions and compared its skills with those of doctors through a survey. They appreciated the AI for providing more time during consultations but preferred doctors for other tasks, for example, diagnostics, recommendation of treatments, and reduction of anxieties.
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Registered trials
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.