Shaping the future of cancer screening with artificial intelligence-empowered multi-cancer early detection
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.
Authors
- Sibo Zhu (ORCID: https://orcid.org/0000-0003-4800-8987)
- Hui Yu (ORCID: https://orcid.org/0000-0002-8511-7296)
- Nuopei Tan (ORCID: https://orcid.org/0000-0001-5173-0458)
- J Liu (ORCID: https://orcid.org/0009-0008-4070-9299)
- W F Chen
- Xiaoying Shi (ORCID: https://orcid.org/0000-0002-9374-6762)
- Yue Liu
- Yongjie Xu
- Changfa Xia
- Yanzhan Yang
- Xiaohui Wu
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Shanghai Medical Information Center (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-07-22
- DOI
- https://doi.org/10.1038/s41746-026-03047-w
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00