This brief explores how AI can support the detection and treatment of rare cancers, where limited patient data and diagnostic complexity pose persistent challenges. It highlights the potential of machine‑learning models to identify subtle diagnostic patterns, improve prognostic accuracy, and optimize individualized therapy planning. Ethical issues, data‑protection requirements, validation hurdles, and the integration of AI tools into clinical workflows are analyzed as key considerations for future adoption.