Artificial Intelligence in Oncology: Integrating Screening, Diagnosis, Prognosis and Treatment

Abstract

Cancer is a global health challenge characterized by complex, adaptive biological processes and substantial mortality rates. Early detection and precise treatment personalization of cancer is elusive despite  all the advances in oncological screening, diagnosis and treatment. Artificial intelligence (AI) emerges as a transformative technology in oncology by leveraging vast and varied datasets, including clinical records, genomic profiles, imaging, and pathology to augment decision-making and improve patient outcomes. AI encompasses machine learning (ML) and deep learning (DL) techniques capable of identifying intricate patterns in high-dimensional data unavailable to human cognition alone. These techniques are applied across the cancer care continuum, enabling improved risk stratification in screening, early diagnosis through radiological and histopathological image analysis, and enhanced prognostic modelling that surpasses traditional statistical methods. AI-driven models facilitate individualized treatment planning with adaptive radiotherapy, robotic surgery assistance, and optimization of chemotherapy and immunotherapy regimens, improving therapeutic efficacy while minimizing toxicity. This review synthesizes evidence across AI-driven screening, diagnosis, prognosis, treatment optimization and drug discovery, highlighting methodologies, clinical performance and future directions for AI in oncology. Key findings demonstrate that AI achieves expert-level performance in imaging-based cancer screening (mammography, colonoscopy, dermoscopy), surpasses traditional statistical models in multi-cancer prognostic prediction, and enables individualized treatment planning through adaptive radiotherapy, robotic surgical guidance, optimization of chemotherapy and immunotherapy regimes. Emerging applications in generative drug engineering and multi-cancer liquid biopsy detection further highlight AI’s expanding translational potential.