Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translation

2026-08-31

AI Summary

本文探讨了AI和NLP在癌症基因组学中的应用挑战，包括证据不一致、可解释性等，并提出通过验证、不确定性感知方法等途径解决这些问题以促进临床转化。

Abstract

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.

Problem

• AI in Cancer Genomics
• Translational Challenges
• Trustworthy Integration
• Clinical Workflows
• Explainability and Uncertainty

Innovation

• trustworthy integration
• uncertainty-aware methods
• interoperable infrastructures
• human oversight