Oncology Reimagined: Harnessing AI-Driven Real-World Evidence for the Next Generation of Cancer Care

Cancer remains a growing global health crisis—with an estimated ~20 million new diagnoses and ~9.7 million deaths worldwide in 2022. Traditional evidence-generation methods, particularly randomized controlled trials (RCTs), are increasingly misaligned with the complexity, heterogeneity, and speed of modern oncology innovation. Meanwhile, real-world data (RWD) captured from electronic health records, registries, claims, imaging, genomics, and patient-reported outcomes is expanding rapidly, providing a transformative opportunity for AI-driven real-world evidence (RWE) to augment and accelerate cancer research. This paper explores how advanced machine learning techniques, multimodal analytics, and unified data ecosystems can generate more precise, scalable, and context-aware evidence for oncology decision-making. We propose a next-generation RWE framework that enhances patient stratification, optimizes treatment selection, improves outcome prediction, and supports regulatory innovation—especially in diverse and resource-constrained markets such as India. Finally, the paper outlines the strategic implications for pharmaceutical R&D, clinical development, payer systems, and global health delivery models. AI-enabled RWE is positioned not just as a tool, but as a new paradigm in oncology evidence, insight generation, and personalized cancer care.