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Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI‐driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes. [ABSTRACT FROM AUTHOR]

The article proposes a strategy to enhance informed consent procedures and data sharing to advance the power of clinical trial data. It considers the potential influence of the U.S. Food and Drug Administration (FDA) Informed Consent guidance in the incorporation of amendments to standard consent procedures. It suggests the inclusion of optional provision for patients to request individual data sharing in future informed consent forms, as well as policies for data collection and storage.