NEXT-GENERATION AI IN ONCOLOGY: EXPLAINABLE FOUNDATION MODELS FOR PRECISION DIAGNOSIS, PROGNOSIS, AND TREATMENT OPTIMIZATION

Abstract

Artificial intelligence (AI) is reshaping modern oncology through the emergence of explainable foundation models capable of integrating heterogeneous biomedical information into intelligent computational ecosystems that support precision diagnosis, prognostic prediction, and individualized therapeutic optimization. Conventional machine learning algorithms have demonstrated remarkable performance in isolated oncology applications; however, their task-specific design, limited interpretability, and dependence on fragmented datasets restrict their widespread clinical implementation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Explainable artificial intelligence (XAI) techniques—including attention visualization, saliency mapping, feature attribution, SHAP (Shapley Additive Explanations), counterfactual reasoning, and uncertainty estimation—provide transparency necessary for clinician confidence, regulatory approval, and trustworthy deployment. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, and agentic AI further strengthen explainable computational oncology through collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of next-generation explainable foundation AI models for precision diagnosis, prognosis, and treatment optimization in modern oncology.