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<p>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.</p>

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Shaping the future of cancer screening with artificial intelligence-empowered multi-cancer early detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Yongjie Xu,
  • Sibo Zhu,
  • Hui Yu,
  • Changfa Xia,
  • Yanzhan Yang,
  • Xiaoying Shi,
  • Jing Liu,
  • Nuopei Tan,
  • Yue Liu,
  • Xiaohui Wu,
  • Wanqing Chen

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.