癌症筛查
筛选试验
计算机科学
钥匙(锁)
医学
人工智能
人工智能应用
数字健康
机器学习
考试(生物学)
癌症
医学检查
精密医学
癌症检测
癌症预防
梅德林
诊断试验
公共卫生
健康筛查
作者
Yongjie Xu,Sibo Zhu,Hui Yu,Changfa Xia,Yanzhan Yang,Xiaoying Shi,Jia-Lu Liu,Nuopei Tan,Yue Liu,Xiaohui Wu,W F Chen
标识
DOI:10.1038/s41746-026-03047-w
摘要
Multi-cancer early detection (MCED) via blood tests expands single-cancer screening. Artificial intelligence (AI) can enhance MCED across the screening lifecycle, from test development to real-world implementation. Key challenges include algorithmic bias, generalizability, overdiagnosis, and governance. Emerging AI paradigms, digital twins, and personal health agents may support dynamic learning health systems. We argue AI should be viewed not merely as a performance booster but as foundational infrastructure for precise cancer screening.
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