Precision Cancer Medicine in the Era of Artificial Intelligence

Abstract

Precision cancer medicine has entered a new era driven by rapid advances in artificial intelligence (AI), computational biology, and high-throughput molecular technologies. While traditional precision oncology primarily relied on genomic profiling to guide targeted therapies, contemporary cancer care increasingly integrates multidimensional biomedical information—including radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, liquid biopsy, electronic health records, and real-world clinical data—to provide individualized diagnosis and treatment. Artificial intelligence has emerged as the central enabling technology capable of interpreting these complex datasets through machine learning, deep learning, computer vision, natural language processing, graph neural networks, multimodal learning, and foundation models. AI-powered systems are transforming every stage of cancer management by improving early diagnosis, prognostic prediction, biomarker discovery, treatment selection, immunotherapy response assessment, adaptive radiation planning, surgical decision-making, drug discovery, and survivorship care. Emerging innovations including generative AI, digital twins, federated learning, explainable AI, single-cell sequencing, spatial biology, and cloud-based intelligent healthcare systems further expand the scope of personalized oncology. Despite these remarkable advances, important scientific, ethical, technical, and regulatory challenges remain regarding data quality, interoperability, algorithmic bias, cybersecurity, transparency, validation, and equitable clinical implementation. This review provides a comprehensive overview of precision cancer medicine in the era of artificial intelligence, highlighting technological foundations, current clinical applications, emerging innovations, and future perspectives that are reshaping modern oncology.[1]