ARCHIVES

Background: Precision oncology aims to individualize cancer prevention, diagnostic stratifications, prognostication, and therapeutic regimens by synthesizing tumor biology with patient-specific clinical, demographic, and multi-omic characteristics. The exponential volume, velocity, and multi-scale complexity of contemporary oncology data—encompassing cross-sectional imaging, digital whole-slide histopathology, high-throughput molecular omics, and longitudinal electronic health records—exceed the cognitive integration capacity of unaided clinical evaluation. Artificial intelligence (AI), comprising supervised and unsupervised machine learning, deep neural networks, transformer architectures, and multimodal data-fusion paradigms, provides computational scaffolding to translate multidimensional inputs into actionable clinical evidence. Objective: To systematically synthesize clinical applications across the cancer-care trajectory, scrutinize methodological and evaluation paradigms, delineate translational and ethical bottlenecks, and outline a rigorous governance framework for implementing AI in precision oncology. Methods: A structured narrative review was conducted across peer-reviewed clinical, bioinformatic, and regulatory literature spanning machine learning, radiomics, computational pathology, multi-omics, predictive oncology, and health AI governance. Evidence was contextualized across four progressive translational evaluation phases: analytical validity, clinical validity, clinical utility, and implementation performance. Results: AI models demonstrate high discriminatory performance in early screening, noninvasive radiogenomic mapping, diagnostic and molecular computational pathology, dynamic biomarker discovery, multi-modal immunotherapy response prediction, adaptive radiation therapy planning, and clinical-trial matching. Concurrently, multimodal fusion systems successfully capture spatial and clonal tumor heterogeneity. However, clinical translation remains constrained by methodological vulnerabilities, including retrospective single-center dataset designs, hidden spectrum bias, feature collinearity, data leakage, black-box uninterpretability, domain shift across clinical institutions, and a lack of prospective, randomized phase III clinical utility trials. Computational discrimination (e.g., elevated area under the receiver operating characteristic curve) does not inherently correlate with clinical net benefit. Conclusion: Realizing the transformative clinical potential of AI in precision oncology necessitates a paradigm shift from purely computational benchmark optimization to robust translational pipelines characterized by transparent reporting (e.g., TRIPOD-AI, STARD-AI), cross-institutional external validation, decision-curve analyses, rigorous privacy-preserving architectures (such as federated learning), and adaptive post-market algorithmic surveillance. Crucially, AI architectures must function as decision-support systems that augment and empower multidisciplinary tumor boards rather than operating as autonomous decision-makers.