ArticleFrontiers in radiology2025
Comparison of artificial intelligence models and physicians in patient education for varicocele embolization: a double-blind randomized controlled trial.
Article in Frontiers in radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large language models (LLMs) appear to be capable of performing a variety of tasks, including answering questions, but there are few studies evaluating them in direct comparison with clinicians. This study aims to compare the performance of artificial intelligence (AI) models and clinical specialists in informing patients about varicocele embolization. Additionally, we aim to establish an evidence base for future hybrid informational systems that integrate both AI and clinical expertise. Methods: In this prospective, double-blind, randomized controlled trial, 25 frequently asked questions about varicocele embolization (collected via Google Search trends, patient forums, and clinical experience) were answered by three AI models (ChatGPT-4o, Gemini Pro, and Microsoft Copilot) and one interventional radiologist. Responses were randomized and evaluated by two independent interventional radiologists using a valid 5-point Likert scale for academic accuracy and empathy. Results: Gemini achieved the highest mean scores for both academic accuracy (4.09 ± 0.50, 95% CI: 3.95-4.23) and higher expert-rated scores for empathetic communication (3.54 ± 0.59, 95% CI: 3.38-3.70), followed by Copilot (academic: 4.07 ± 0.46, 95% CI: 3.94-4.20; empathy: 3.48 ± 0.53, 95% CI: 3.33-3.63), ChatGPT (academic: 3.83 ± 0.58, 95% CI: 3.67-3.99; empathy: 2.92 ± 0.78, 95% CI: 2.70-3.14), and the comparator physician (academic: 3.75 ± 0.41, 95% CI: 3.64-3.86; empathy: 3.12 ± 0.82, 95% CI: 2.89-3.35). ANOVA revealed statistically significant differences across groups for both academic accuracy (F = 6.181, Conclusions: AI models, particularly Gemini, received higher ratings from expert evaluators compared to the comparator physician in patient education regarding varicocele embolization, excelling in both academic accuracy and empathetic communication style. These preliminary findings suggest that AI models hold significant potential to complement patient education systems in interventional radiology practice and provide compelling evidence for the development of hybrid patient education models.
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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.