The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.
近年来，多癌种早期检测（MCED）的理念与政策受到全球各国政府的高度重视。在人工智能技术蓬勃发展的时代背景下，MCED与人工智能的融合已成为主流趋势，催生了大量MCED人工智能产品。然而，由于检测靶标与人工智能技术均存在异质性，MCED人工智能产品的整体多样性仍然十分显著。检测靶标类型涵盖蛋白质生物标志物、游离DNA或多种生物标志物的组合。在人工智能模型开发过程中，研究者采用不同的模型训练方法，包括病例对照研究数据集或真实世界癌症筛查数据集。验证技术也呈现多样化特征，例如交叉验证、空间分层验证和时间分层验证等。所有因素均对MCED人工智能的预测效能产生显著影响。在完成人工智能模型开发后，将MCED人工智能应用于临床实践仍面临诸多挑战，包括预测报告的呈现方式、潜在肿瘤位置与类型的判定，以及临床随访与治疗等癌症相关信息的处理。本研究综述了当前市场上若干成熟的MCED人工智能产品，从血清生物标志物检测、MCED人工智能训练/验证及临床应用三个维度解析其构成要素。本综述系统阐述了现有MCED人工智能产品在各阶段面临的挑战，为把握该领域持续发展动态与突破现存障碍提供了重要参考。
肿瘤(癌症)患者之家