Artificial Intelligence for Precision Cancer Diagnosis, Prognosis, and Therapy

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine. The increasing complexity of cancer biology, characterized by genomic instability, tumor heterogeneity, dynamic interactions with the tumor microenvironment, and diverse therapeutic responses, has created an urgent need for advanced computational approaches capable of supporting individualized clinical decision-making. Artificial intelligence (AI) has emerged as a transformative technology in modern oncology by enabling automated analysis of large-scale biomedical data derived from radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, electronic health records, and wearable health technologies. Through machine learning, deep learning, computer vision, natural language processing, multimodal learning, and foundation models, AI is enhancing cancer diagnosis, prognostic prediction, therapeutic selection, response monitoring, and survivorship care. Unlike conventional statistical methods, AI systems continuously learn from heterogeneous clinical data to generate personalized predictions that support precision oncology. Recent innovations have further expanded AI applications into radiogenomics, computational pathology, liquid biopsy analysis, clinical decision support, drug discovery, and adaptive treatment planning. Despite these advances, significant challenges remain regarding data quality, model interpretability, algorithmic bias, cybersecurity, interoperability, regulatory approval, and ethical governance. This review provides a comprehensive overview of artificial intelligence technologies driving precision cancer diagnosis, prognosis, and therapy while discussing emerging innovations and future clinical applications that may redefine personalized oncology.[1]