CAUSAL ARTIFICIAL INTELLIGENCE IN ONCOLOGY: ENHANCING EXPLAINABILITY AND CLINICAL DECISION-MAKING IN PRECISION MEDICINE

Abstract

Causal artificial intelligence (AI) is emerging as a transformative paradigm in precision oncology by enabling computational systems to move beyond correlation-based prediction toward causal reasoning that supports transparent, reliable, and clinically actionable decision-making. Conventional machine learning and deep learning models have achieved remarkable success in cancer diagnosis, prognosis, and therapeutic prediction; however, many remain limited by their inability to distinguish causal relationships from statistical associations, thereby reducing interpretability, generalizability, and clinical trust. Recent advances in causal inference, foundation AI models, multimodal transformer architectures, graph neural networks, structural causal models, Bayesian networks, reinforcement learning, and generative artificial intelligence have enabled integration of 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 into explainable computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, treatment optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support through causal reasoning rather than correlation alone. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen causal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding causal model validation, multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of causal artificial intelligence in oncology, emphasizing its role in enhancing explainability and clinical decision-making for precision medicine.