Intelligent Medicine · Published 2026-03-03 · DOI 10.1016/j.imed.2026.02.001
Malignant tumors pose a significant global health challenge. While established screening methods exist, they are largely site-specific, limiting comprehensive prevention. Multi-cancer screening, which detects multiple cancers simultaneously, offers substantial advantages including optimized sample utilization, reduced participant costs, enhanced efficiency, and improved resource allocation. Artificial intelligence (AI) has emerged as a transformative technology, revolutionizing healthcare by analyzing complex biomedical data and enhancing diagnostic accuracy. Integrating AI into multi-cancer screening holds immense potential for advancing early cancer detection. This review provides a comprehensive overview of AI-driven multi-cancer screening. We examine foundational technologies and current applications, including biomarker data and medical imaging data analysis, as well as core AI techniques like machine learning, deep learning, natural language processing, explainable AI and essential preprocessing steps. We assess key technical bottlenecks such as data sparsity, model generalizability, false positives and false negatives, and affordability, alongside solutions like transfer learning, federated learning, and Bayesian optimization. Additionally, we highlight clinical validation, regulatory approval, and ethical considerations for multi-cancer screening. Furthermore, we explore future prospects, envisioning enhanced accuracy and expanded coverage, deeper multi-modal data fusion, personalized and dynamic screening, and intelligent decision-support systems with improved accessibility. We also outline targeted recommendations for developing countries conducting AI-driven multi-cancer screening, building on global best practices while adapting to local realities including those in China. This review offers a forward-looking perspective on how AI will evolve multi-cancer screening into a more personalized, dynamic and accessible cornerstone of cancer prevention.
Abstract from DOAJ. Public domain (CC0 1.0).
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