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人工智能在良性前列腺增生症管理中的当前作用:系统性审查

Manfredi B Sequi¹, Antonio L Pastore¹, Yazan Al Salhi¹

¹Urology Unit, Department of Medico-Surgical Sciences and Biotechnologies, Faculty of Pharmacy and Medicine, Sapienza University, Latina, Italy.

Minerva urology and nephrology

|February 16, 2026

概括

人工智能 (AI) 在治疗良性前列腺增生症 (BPH) 方面显示出巨大的前景,改善了诊断和患者教育. 需要进一步的研究,以克服数据限制等挑战,并确保广泛的临床采用,以获得更好的患者结果.

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:

人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 正在改变医疗保健,包括泌尿病学.
本综述侧重于AI在良性前列腺增生症 (BPH) 管理中的应用.

研究的目的:

检查人工智能在BPH管理中的当前应用.
评估AI在提高BPH诊断,治疗和患者教育方面的潜力.

主要方法:

在PubMed,Embase,Web of Science,IeeeXplore和Cochrane数据库中进行了系统的文献搜索,直到2024年12月.
研究被分类为大型语言模型 (LLM) 用于患者教育和临床AI应用 (生物标志物识别,诊断,成像,治疗预测,组织学).
使用QUADAS-2,PROBAST和ROBINS-I工具进行了偏差风险评估.

主要成果:

关于LLM的四项研究 (ChatGPT-3.5,ChatGPT-4,New Bing Chat) 显示了在回答BPH问题时的高准确性,用于患者教育.
关于临床AI应用的19项研究表明,AI在生物标志物识别,功能评估,成像,严重性诊断,治疗预测和组织学检查方面的有效性.
人工智能提高了诊断准确度,预测了尿流模式,增强了前列腺成像,提供了准确的严重程度评估,并预测了治疗反应.

结论:

人工智能在BPH管理方面显示出巨大的潜力,提高了诊断准确度,个性化治疗和改善患者参与度.
挑战包括数据异质性,有限的前景验证,道德问题和监管障碍.
未来的研究应该优先考虑多机构合作,标准化数据集和现实世界的验证,以便广泛采用临床.

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