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Transforming urological care: Physician and patient insights on artificial intelligence integration

2026·0 Zitationen·Current UrologyOpen Access
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5

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2026

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Abstract

Background: The integration of artificial intelligence (AI) into urological practice offers promising advancements in diagnostic, prognostic, and therapeutic decision-making, with applications spanning the field of urology. Because both patients and physicians have started adapting to AI in urology, this study examined their understanding of AI and their converging and diverging perspectives. Materials and methods: An Institutional Review Board-approved survey was created to assess awareness and perspectives on AI within the urological community across the United States. Percentages were used to quantify the comparisons, and statistical tests, as appropriate, were applied for significance, with p <0.05 considered statistically significant. Surveys were distributed via email to patients and urologists. Results: Among 380 participants (199 physicians and 181 patients), both groups shared a baseline unfamiliarity with AI in general (59.3% vs. 65.8%; p = 0.2) and AI in healthcare (71.4% vs. 76.8%; p = 0.2). The majority of each group expressed optimism about AI’s clinical utility (61.3% vs. 74.6%; p = 0.006), but held mixed trust in its accuracy, with physicians showing greater mistrust (49.7% vs. 34.2%; p = 0.001). Ethical and privacy concerns were notable, with more physicians than patients emphasizing ethical issues (38.7% vs. 22.1%; p = 0.001) and privacy risks (50.8% vs. 42.0%; p = 0.2). Both groups favored shared accountability for AI-driven outcomes (71.3% vs. 63.3%; p = 0.2) and human oversight (78.4% vs. 66.9%; p = 0.01), underscoring the need for cautious AI integration in urological practice. Conclusions: This study revealed the alignment between physicians and patients regarding the potential of AI in urological practice, along with shared concerns about its reliability, ethics, and oversight.

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Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareExplainable Artificial Intelligence (XAI)
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