Artificial Intelligence in Precision Oncology: Current Advances, Clinical Applications, and Future Directions

Abstract

Background: Precision oncology has transformed modern cancer management by integrating molecular profiling, advanced imaging, and individualized therapeutic strategies to improve clinical outcomes. However, the increasing complexity of multidimensional clinical, radiological, pathological, genomic, transcriptomic, and real-world patient data has created significant analytical challenges that exceed traditional computational approaches. Artificial intelligence (AI) has emerged as a transformative technology capable of extracting clinically meaningful patterns from heterogeneous biomedical datasets and supporting personalized cancer diagnosis, prognosis, treatment selection, and disease monitoring. Recent advances in machine learning, deep learning, transformer architectures, multimodal learning, explainable AI, and large language models have substantially expanded AI applications across oncology. AI systems are increasingly utilized in digital pathology, radiology, radiomics, genomics, immunotherapy prediction, biomarker discovery, clinical decision support, and drug development. These technologies facilitate earlier cancer detection, improved tumor characterization, accurate risk stratification, prediction of therapeutic response, and optimization of precision treatment strategies. Furthermore, integration of multimodal datasets enables comprehensive patient-specific models capable of supporting individualized clinical decisions. Despite remarkable progress, important challenges remain regarding data quality, algorithmic bias, interpretability, privacy protection, regulatory approval, and clinical implementation. This review summarizes recent advances in artificial intelligence for precision oncology, discusses current clinical applications across multiple oncology domains, highlights existing limitations, and explores future opportunities for AI-driven personalized cancer care.