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Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translation
Bahar İlgen, Yiannos Tolias, Denise Kühnert, Paraskevi Papadopoulou, Magnus Westerlund, Dominik Heider, Katharina Ladewig, Georges Hattab
TL;DR
AI and NLP have broad utility in cancer genomics, but routine clinical translation remains slow because trustworthy workflow integration is unresolved. This review examines the pipeline and its interacting failure domains, concluding that clinical adoption depends on validation, uncertainty awareness, interoperability, governance, and sustained human oversight.
Problem
Cancer genomics AI has advanced, but translating systems into routine oncology remains constrained by evidence limitations and the requirements of trustworthy clinical integration.
Method
The review uses a systems-level conceptual framework to examine NLP and AI applications, four interacting translational failure domains, and pathways for responsible integration across the AI lifecycle.
Results
NLP-enabled AI supports literature mining, variant interpretation, multimodal integration, knowledge graph construction, and clinical trial matching, but few systems achieve sustained routine deployment.
Takeaways & Limitations
Trustworthy clinical use requires clinically interpretable evidence, uncertainty-aware methods, interoperable infrastructures, governance, validation, and human oversight throughout deployment and maintenance.
Takeaways & Limitations
Computational advances cannot fully compensate for scarce validated biological evidence, especially for rare variants, so functional studies and clinical validation remain necessary.
Abstract
from arXiv · showhide
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.
1 Introduction
Cancer genomics AI has expanded across evidence synthesis, variant interpretation, and multimodal clinical workflows, but routine oncology adoption remains limited by interconnected translational barriers. The review therefore frames trustworthy integration as a systems problem spanning evidence, explainability, governance, and interoperability.
- Research utility: Cancer genomics AI now supports literature mining, variant interpretation, clinical trial matching, knowledge graph construction, and multimodal data integration.
- Translational framework: The review organizes trustworthy clinical integration around four interconnected failure domains: evidence inconsistency, explainability and uncertainty, data governance, and interoperability.
- Translational framework: Routine translation requires overcoming interacting barriers so AI systems become reliable, transparent, and clinically integrated in precision oncology.
- Research utility: Large-scale sequencing and expanding biomedical resources have shifted the challenge from generating genomic data to integrating heterogeneous evidence into clinically actionable knowledge.
- Clinical translation: Few systems progress beyond experimental or retrospective settings because clinical deployment must address evolving evidence, heterogeneous data environments, regulation, interpretability, reproducibility, interoperability, and governance.
3 Evidence Inconsistency and Clinical Reliability
NLP-enabled AI can retrieve and synthesize evidence for variant interpretation, but trustworthy clinical use remains constrained by inconsistent evidence, rare-variant scarcity, and limited functional validation. Reliable interpretation therefore requires computational support alongside experimental evidence, curation, and expert adjudication.
- Variant interpretation: NLP integrates literature, clinical records, and curated knowledge bases to support identification and prioritisation of clinically actionable variants.
- Evidence inconsistency: Improved NLP models can retrieve and synthesise uncertainty or conflict in biomedical evidence but are unlikely to resolve those underlying inconsistencies.
- Functional validation: Computational predictions cannot substitute for experimental validation when variant effects inform clinical decisions.
- Rare variants: Rare-variant interpretation remains limited by incomplete biological validation, database gaps, and underrepresentation of rare variants and populations.
- Functional validation: Trustworthy interpretation combines AI-assisted annotation, functional assays, and expert interpretation rather than substituting computation for experimental validation.
- Clinical reliability: Conflicting findings and inconsistent database annotations can propagate through evidence integration into downstream predictions and clinical recommendations.
4 Explainability and Uncertainty in Genomic Reasoning
Trustworthy genomic reasoning requires explanations and uncertainty communication that let clinicians inspect evidence, recognize limitations, and calibrate reliance. The review distinguishes uncertainty in genomic evidence from uncertainty introduced by models and emphasizes their integration into human clinical workflows.
- Explainability: Variant recommendations require explanations traceable to supporting literature, databases, and clinical observations, with argumentation frameworks providing structured justifications.
- Explainability: Feature-attribution and attention-based explanations may capture correlations rather than biological mechanisms and can disagree despite identical predictions.
- Explainability: Interpretability supports clinician verification and regulatory accountability but cannot compensate for unreliable evidence, poor calibration, or insufficient clinical validation.
- Genomic uncertainty: Genomic uncertainty arises from incomplete knowledge, probabilistic and conflicting evidence, heterogeneous resources, VUS, limited validation, and population-specific differences.
- Model uncertainty: LLMs may produce fluent but incorrect or overconfident summaries when biomedical evidence is limited or conflicting.
- Clinical decision support: Appropriate reliance requires calibrated uncertainty, transparent limitations, and human–AI collaboration that supports rather than replaces clinical judgement.
- Uncertainty communication: Layered uncertainty representations should combine concise decision-oriented summaries with detailed evidence traces, limitations, and context-specific caveats.
5 Data Governance and Reproducibility Barriers
Clinical translation depends on data quality, governance, reproducibility, and interoperability rather than computational capability alone. Responsible deployment also requires lifecycle oversight, privacy safeguards, representative data, meaningful consent, and human accountability.
- Data standardisation: Cancer genomics AI must integrate heterogeneous clinical, genomic, literature, and molecular data whose formats, terminologies, and ontologies differ across sources.NLP can harmonise local terms and metadata, but performance and generalisability depend on consistent annotation and interoperable data infrastructures.
- Data standardisation: Standards including OMOP, HL7 FHIR, LOINC, UMLS, HGVS, FAIR, and international genomic initiatives provide harmonisation foundations, but adoption remains uneven.Uneven implementation limits cross-institutional data sharing and reproducibility.
- Governance and reproducibility: Governance must cover access, provenance, accountability, quality assurance, secure environments, and dataset fitness throughout the AI lifecycle.The EHDS introduces standardised labels for completeness, consistency, representativeness, and quality-management procedures, positioning governance as infrastructure rather than an afterthought.
- Emerging AI systems: Foundation models create additional governability risks because changing training data, retrieval resources, and model versions complicate reproducibility, traceability, and independent validation.Clinical systems require continuous knowledge updating and revalidation because evidence and recommendations can become outdated.
- Privacy, bias, and stewardship: Cancer genomics requires privacy safeguards, lawful data use, community consideration, representative training data, bias audits, fairness-aware methods, and explicit, contextual, revocable consent.Algorithmic mitigation cannot substitute for representative data, while consent should include transparency, human oversight, audit, and redress.
6 Interoperability and Multimodal Fragmentation
Interoperability and multimodal integration fail through heterogeneous infrastructures, lossy semantic mappings, evolving meanings, organisational gaps, and uneven model governance. Trustworthy integration therefore requires failures to be detectable, mappings to be maintained and reviewed, and data completeness and provenance to be established.
- Multimodal fragmentation: NLP structures narratives and literature for integration with genomic, imaging, and molecular data, but cannot resolve incompatible infrastructures, acquisition pipelines, incomplete records, or absent exchange frameworks.Patients may have rich data in one modality but missing or asynchronous records in others, complicating multimodal learning and external validation.
- Failure detectability: Interoperability failures can remain silent when concept collapses, unit mismatches, version discrepancies, or incorrect codes pass schema validation and enter confident outputs.Trustworthy systems must make failures detectable at the point where they can otherwise cause downstream harm.
- Semantic interoperability: Semantic harmonisation is lossy: many-to-one mappings can discard laterality, qualifiers, and local subtypes, while mappings between proliferating standards introduce further loss.Models cannot recover distinctions removed during harmonisation, and end-to-end validation is rare.
- Versioning and measurement: Genomic meaning changes across terminology, staging, response-criteria, and reference-genome revisions, so unchanged files may acquire different clinical interpretations over time.A standard alone cannot ensure equivalence because assay-dependent variables such as TMB and radiomic features can differ across sites.
- Organisational interoperability: Organisational interoperability requires explicit commitments for knowledge-base curation, report accountability, validation reruns, mapping maintenance, and sustained specialist resources.Resource asymmetry can favour large cancer centres and reintroduce representativeness bias even when analysis is federated.
- Governance of harmonisation: Legal constraints take precedence over technical sophistication, while model-assisted harmonisation adds reproducibility debt, valid-looking mapping errors, and subgroup-correlated fairness risks.Mitigation requires gold-standard benchmarks, human review, reviewer records, and provenance for model identity, version, configuration, and confidence.
7 Clinical Implementation of High-Risk AI
Clinical cancer genomics AI is treated as high-risk when integrated into regulated medical products, so trustworthy use requires lifecycle governance beyond predictive performance. Human oversight, traceability, validation, and post-deployment monitoring are necessary because clinical evidence and system behavior evolve.
- High-risk cancer genomics AI must satisfy requirements for safe deployment, transparency, human oversight, and lifecycle monitoring beyond predictive performance.
- Compliance requires risk management, technical documentation, data governance, robustness, accuracy, and cybersecurity.
- Model reuse can create additional documentation duties for downstream providers, with accountability shared across the model lifecycle.
- Clinicians should critically evaluate AI recommendations because genomic interpretation and treatment decisions involve uncertain, incomplete, and evolving evidence.
- Meaningful oversight requires understandable rationales, recognition of unreliable predictions, clinician challenge or override, evidence provenance, and independent verification.
- Accountability requires defined responsibilities, documentation, audit trails, traceability, external audits, and redress pathways across developers, providers, and clinical users.
- Post-deployment monitoring should detect dataset shift, degradation, adverse events, and calibration changes as variants, evidence, and guidance evolve.
- Trustworthy deployment is continuous governance requiring version control, provenance tracking, periodic revalidation, monitoring, and maintenance rather than a one-time regulatory milestone.
8 Critical View and Pathways to Trustworthy Clinical Translation
The review frames trustworthy clinical translation as a systems-level problem created by interacting weaknesses in evidence, explanation, governance, reproducibility, and interoperability. Existing systems address limited portions of this landscape, so responsible integration requires coordinated principles across the translational pathway.
- Interacting weaknesses across evidence inconsistency, explainability and uncertainty, data governance and reproducibility, and interoperability can propagate into downstream clinical decisions.
- Representative systems target specific functions, including variant classification, literature linkage, entity annotation, clinical interpretation, trial matching, and drug–gene interaction aggregation.
- These systems are illustrative rather than exhaustive or comparative, and their limitations largely reflect intended scope rather than implementation deficiencies.
- Collectively, the systems cover only one or two failure domains, while none addresses all four, indicating a structural ecosystem-level gap.
- The framework links translational failure domains with their clinical consequences and guiding principles for trustworthy clinical translation.
- Evidence quality, explainability, reproducibility, interoperability, governance, and human oversight reinforce one another across the translational pathway.
- Responsible integration therefore requires scientific, technical, clinical, and governance challenges to be addressed together through a systems-level approach.
1. Prioritize evidence quality over model complexity
Advanced AI models cannot compensate for incomplete, conflicting, or poorly validated genomic evidence.
- Advanced AI models cannot compensate for incomplete, conflicting, or poorly validated genomic evidence.
2. Promote explainable and uncertainty-aware AI
Variant classifications and AI-generated recommendations should make their reasoning, evidence origins, and uncertainty visible to support trustworthy interpretation.
- Variant classifications and AI-generated recommendations should include transparent explanations, evidence provenance, and calibrated uncertainty estimates.
3. Integrate functional validation into AI-supported interpretation
Computational predictions in cancer genomics should be complemented by experimental evidence whenever possible.
- Experimental evidence should complement computational predictions whenever possible.
4. Promote reproducibility and transparent evaluation
Reproducible clinical AI requires evaluation across diverse datasets and institutions, transparent reporting of limitations, and seamless integration of genomic, clinical, and biomedical knowledge sources.
- Models should be evaluated across diverse datasets and institutions.Such evaluation should include clear reporting of limitations and validation procedures.
- Clinical adoption requires seamless integration of genomic, clinical, and biomedical knowledge sources.
6. Embed governance throughout the AI lifecycle
Trustworthy clinical translation requires governance, transparency, expert oversight, proportional validation, and structural investment across the AI lifecycle. These priorities must work together across institutions and healthcare systems to support clinically trustworthy evidence in routine oncology practice.
- Privacy, accountability, transparency, and regulatory compliance should be incorporated from development through deployment.
- AI systems should support rather than replace expert clinical judgment.
- Oversight and validation requirements should be proportional to the potential clinical consequences of AI-assisted decisions.
- Structural priorities: Cross-institutional and multilingual data infrastructures are needed for reproducibility and interoperability.Standards such as OMOP and HL7 FHIR, together with initiatives such as the EHDS, provide a foundation whose value depends on broader adoption.
- Structural priorities: Functional validation, explainable design, infrastructure, and equitable governance together provide a roadmap for trustworthy clinical implementation.Weaknesses in one priority cannot be fully compensated for by progress in another.
- Routine clinical adoption will depend on addressing translational barriers and fostering collaboration among computational scientists, clinicians, molecular biologists, regulators, healthcare institutions, and patients.
- Future AI systems in cancer genomics will be judged by whether their evidence can be interpreted, validated, governed, and sustained in routine oncology practice.