A responsible artificial intelligence framework for translational readiness of machine learning biomarker models in clinical decision support

Abstract Machine learning (ML) has expanded biomarker modelling across molecular, imaging, laboratory, electronic health record, and multimodal data, yet strong retrospective performance does not by itself establish clinical readiness. This structured narrative review examines the evidence required to translate ML-based biomarker models into clinical decision-support systems. PubMed, Scopus, Web of Science, and Google Scholar were searched for peer-reviewed literature addressing biomarker validation, external validation, calibration, interpretability, workflow integration, governance, and lifecycle monitoring. Seventy core publications were qualitatively mapped to five readiness domains: methodological, clinical, operational, governance, and post-deployment. The synthesis identified recurring weaknesses in external validation, calibration, feature stability, clinical utility, workflow fit, subgroup assessment, accountability, and monitoring for data and performance drift. Building on these findings, the article proposes a five-domain translational-readiness framework and a stage-gated, evidence-informed assessment guide for evaluating whether a biomarker model is suitable for clinical decision support. The framework distinguishes predictive performance from broader implementation readiness. It emphasises modality-specific risks, including assay and batch variability in molecular models, acquisition and device effects in imaging models, documentation and missingness patterns in electronic health record models, and incomplete or temporally misaligned inputs in multimodal models. The framework is intended as a structured evaluation aid rather than a validated scoring instrument. Prospective testing is needed to determine whether its use improves model evaluation, governance decisions, and patient safety.