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在人工智能时代,数字双胞胎用于尿瘤学个性化治疗

Magdalena Görtz^1,2, Carlos Brandl³, Anna Nitschke³

¹Junior Clinical Cooperation Unit 'Multiparametric Methods for Early Detection of Prostate Cancer', German Cancer Research Center (DKFZ), Heidelberg, Germany. magdalena.goertz@dkfz-heidelberg.de.

Nature reviews. Urology

|October 10, 2025

概括

数字双胞胎,或数字患者模型,通过整合健康数据,显示出个性化泌尿瘤护理的前景. 克服数据隐私和可解释性等挑战是临床使用的关键.

科学领域:

尿瘤学尿瘤学
数字健康数字健康
人工智能的人工智能

背景情况:

数字双胞胎,来自多式联络健康数据的患者特定模型,正在获得医疗保健的引力.
这些模型为个性化护理提供了潜力,特别是在泌尿瘤学中.
目前在泌尿瘤学中的应用是有限的,只有很少的可行性和实用性评估.

研究的目的:

探索数字双胞胎在改善泌尿瘤学内个性化护理方面的潜力.
突出人工智能在增强数字双胞胎开发和应用中的作用.
确定在临床实践中实施数字双胞胎的挑战和机会.

主要方法:

将多式健康数据 (临床,基因组,成像,病理学) 集成到数字双胞胎模型中.
利用人工智能进行数据集合成,改善模型准确性和实时决策支持.
评估数字双胞胎在泌尿瘤学的可行性和临床实用性.

主要成果:

数字双胞胎可以模拟器官行为,疾病进展和治疗反应.
人工智能可以增强数字双胞胎的数据集成和模型准确性.
挑战包括数据集成,隐私,计算需求和道德考虑.

结论:

数字双胞胎具有通过改善患者分层和治疗计划来推进精确性泌尿瘤学的巨大潜力.
克服技术,伦理和可解释性障碍对于临床实施至关重要.
跨学科的合作和透明度对于实现数字双胞胎在泌尿瘤学的未来至关重要.

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