Cancer continues to pose one of the greatest challenges to global health, characterized by biological complexity, heterogeneity, and therapeutic resistance. Over the past decades, oncology has evolved from broad cytotoxic interventions to molecularly targeted and immune-based therapies, yet substantial unmet clinical needs persist. The advent of artificial intelligence (AI) marks a transformative leap in this continuum, enabling the integration and interpretation of multi-dimensional biomedical data with unprecedented depth and accuracy. AI-driven algorithms now play pivotal roles in early cancer detection, histopathological and radiomic analysis, biomarker discovery, and predictive modeling of therapeutic responses. Moreover, AI is accelerating drug discovery pipelines and facilitating adaptive, patient-specific treatment strategies that transcend traditional evidence-based paradigms.This review synthesizes current advancements at the interface of AI and oncology, outlining how machine learning, deep learning, and computational modeling are reshaping diagnostic precision, therapeutic optimization, and clinical decision-making. We further explore emerging challenges, including data heterogeneity, algorithmic transparency, and ethical implementation in real-world settings. By integrating computational intelligence with precision medicine, the future of cancer care is envisioned as predictive, preventive, and personalized — where human expertise and AI coalesce to transform outcomes and redefine the boundaries of cancer treatment.
Maurya et al. (Wed,) studied this question.