Aug 31, 2026 · cs.AIJ/K move · Enter open · S save
Bahar İlgen, Yiannos Tolias, Denise Kühnert, Paraskevi Papadopoulou+4
1*Centre for Artificial Intelligence in Public Health Research (ZKI-PH),2026 Robert Koch Institute, Nordufer 20, Berlin, 13353, Germany. · 2Directorate-General for Health and Food Safety, European Commission, Rue de Loi 200, Brussels, B-1049, Belgium.Aug · Department of Natural Sciences, School of Science and Technology, Deree-The American College of Greece, Gravias 6, Athens, 15342,31 Greece. · 4˚Abo Akademi University, Turku, Finland. · 5Arcada University of Applied Sciences, Helsinki, Finland.
Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relevant to cancer genomics, yet their translation into routine clinical oncology has been comparatively slow. The central challenge is not computational capability alone, but trustworthy integration into clinical workflows. This review examines how NLP and AI support the cancer genomics pipeline, from literature mining and automated variant interpretation to clinical trial matching, knowledge graph construction, and multimodal data integration. We identify four interrelated translational failure domains: evidence inconsistency, explainability and uncertainty, data governance and reproducibility, and interoperability. Rather than considering these challenges in isolation, we take a systems-level view, focusing on their interaction across the translational pathway. We propose a conceptual framework and roadmap for addressing these domains through rigorous validation, uncertainty-aware methods, interoperable infrastructures, regulatory alignment, and human oversight across the AI lifecycle. Progress toward routine clinical use will depend less on further improving model capability than on systematically addressing these interacting failure domains from development through deployment and post-deployment monitoring.