A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics...

Cancer diagnostics is being reshaped by rapid advances in artificial intelligence, yet a persistent gap separates computational performance from clinical trust. Systematic reviews confirm that 83% of XAI studies in oncology excluded clinicians from development or evaluation, 87% lacked rigorous assessment of XAI explanations, and no universally accepted quality metrics for XAI outputs currently exist. Concurrently, generative AI (GenAI) is entering oncology at an unprecedented pace, yet it operates largely without formal interpretability governance.

This review aimed to (i) synthesize the documented gaps in XAI applications for cancer diagnostics from peer-reviewed literature, (ii) evaluate the state and limitations of GenAI in oncological settings, and (iii) propose a conceptual framework of three GenAI-powered, XAI-governed agents designed to address these gaps within a systems biology context.

A narrative synthesis of peer-reviewed literature published between 2020 and 2026 across PubMed, Scopus, and Web of Science identified four critical, recurrent gaps: systematic exclusion of clinicians from XAI development, the absence of standardized evaluation metrics, incomplete cross-omics explanations, and the near absence of lifestyle-driven XAI models for cancer risk. GenAI is accelerating in oncology but introduces additional safety concerns, including hallucinations, non-deterministic outputs, and the illusion of transparency through chain-of-thought reasoning.

Three specialized agents are proposed: a Multi-Omics XAI Agent (MO-XAI Agent) for cross-layer biological data interpretation, a Clinician Trust and Communication Agent (CTC Agent) for structured explanation translation and quality scoring, and a Lifestyle-Driven Cancer Risk Stratification Agent (LRS Agent) for modifiable risk factor analysis. Each agent uses a large language model as the computational backbone with SHAP, LIME, or graph-level XAI methods as governance layers.

To our knowledge, this framework represents the first conceptual architecture to integrate agentic GenAI with systematic XAI governance for cancer diagnostics, offering a clinically grounded, biologically interpretable, and regulatorily aligned design specification for future implementation research.