Aneesha Mallu Reddy, Gurleen Kaur, Vincent Sean D. Ribaya, Elizabeth Laurize A. Ribaya, Mallu Chenna Reddy, Tariq Shah, Dheeraj Shinde, Gurparsad Singh Suri
Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized approaches from diagnosis to treatment. This review explores the transformative role of AI across the cancer care continuum, highlighting its contributions, challenges, and future directions. AI has significantly advanced cancer detection and diagnosis by improving the interpretation of medical imaging (CT, MRI, PET scans, digital pathology) and liquid biopsies, allowing for early and accurate identification of tumors and biomarkers. In genomics and molecular profiling, AI facilitates the analysis of large-scale sequencing data to uncover actionable mutations and support targeted therapy decisions. This review also examines AI-powered prognostic models that integrate clinical, genomic, and electronic health record data to predict outcomes such as survival rates and recurrence risks, allowing for more precise treatment planning. In the therapeutic landscape, AI aids in optimizing radiation dosing, guiding surgical interventions, and predicting individual responses to chemotherapy, immunotherapy, and targeted treatments, thereby reducing uncertainty and improving outcomes. Key limitations, such as data privacy concerns, algorithmic bias, model opacity, and integration hurdles are discussed, along with strategies to address them, including explainable AI, standardized validation, and clinician training. Looking ahead, innovations like federated learning, generative AI for drug discovery, and multimodal data integration are poised to enhance precision oncology further. By synthesizing current developments and emerging trends, this review underscores the potential of AI to drive equitable, efficient, and personalized cancer care on a global scale.