Investigates the role of AI in improving cancer screening, highlighting foundational implications.
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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Xu et al. (2026) studied this question.
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