EXPLAINABLE MULTIMODAL ARTIFICIAL INTELLIGENCE IN PRECISION ONCOLOGY: FROM BIOMARKER DISCOVERY TO CLINICAL ADOPTION

Abstract

Explainable multimodal artificial intelligence (XAI) is emerging as a transformative paradigm in precision oncology by combining transparent computational reasoning with comprehensive integration of heterogeneous biomedical data. Although deep learning and foundation artificial intelligence (AI) models have achieved remarkable diagnostic and prognostic performance, their limited interpretability has constrained widespread clinical adoption due to concerns regarding transparency, trust, accountability, and regulatory acceptance. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, foundation AI models, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. Explainable AI techniques including attention visualization, saliency mapping, SHAP (Shapley Additive Explanations), integrated gradients, counterfactual reasoning, uncertainty estimation, concept attribution, and causal inference enable transparent interpretation of multimodal predictions while supporting biomarker discovery, therapeutic optimization, adaptive disease monitoring, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, agentic AI, federated learning, retrieval-augmented generation, cancer knowledge graphs, cloud-native healthcare platforms, and digital health ecosystems further strengthen explainable oncology by enabling collaborative, privacy-preserving, trustworthy, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, algorithmic bias, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of explainable multimodal artificial intelligence in precision oncology, emphasizing its role from biomarker discovery to routine clinical adoption.