消除机器学习在内分泌学中的神秘性 - 了解模型,应用和临床影响:EAU内分泌学的综述
Chady Ghnatios1, Rose Mary Attieh2, Frederic Panthier3
1University of North Florida, Jacksonville, Florida, USA; Endourology Technology Section, European Association of Urology, Arnhem, The Netherlands.
Current opinion in urology
|October 21, 2025
概括
机器学习 (ML) 和人工智能 (AI) 在泌尿病学中提供了显著的潜力,尽管通过医生培训是有限的. 本综述指导了ML/AI在泌尿病学中的使用,旨在通过更好的决策来增强患者护理.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 和人工智能 (AI) 越来越多地被用于医学,但泌尿科医生往往缺乏对这些技术的培训.
- 了解ML/AI的能力对于其在泌尿病实践中的有效应用至关重要.
研究的目的:
- 作为实施泌尿病学ML的指南.
- 为医疗专业人员解密ML和AI算法.
- 审查ML在泌尿病学中的当前和潜在应用.
主要方法:
- 在泌尿病学中对ML应用的文献综述.
- 分析ML算法功能,包括诊断,预后和治疗优化.
- 探索先进的人工智能技术,如代理的人工智能.
主要成果:
- 许多研究表明,在泌尿病学中,ML的性能与人类专家相美,主要是在诊断和预后方面.
- 对于决策和治疗优化的先进人工智能在泌尿病学中未得到充分利用.
- 民主化机器学习技术可以加速采用并改善患者护理.
结论:
- 这项工作旨在消除泌尿病学应用的ML工具的神秘性.
- 促进适当的ML工具的知情采用是关键目标.
- 提出了一份路线图,以利用ML来提高泌尿病学的患者护理.
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