What does this research mean for the field?

Strong retrospective performance is insufficient for the clinical readiness of machine learning biomarker models, necessitating a comprehensive five-domain framework that evaluates methodological, clinical, operational, governance, and post-deployment factors. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To synthesize the evidence needed to translate machine learning-based biomarker models into clinical decision-support systems and establish a structured readiness framework.

August 30, 2026Discover Artificial IntelligenceOpen Access

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

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Authors

VHVahid Habibzadehomran

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Implication

Narrative review demonstrates widespread translational deficits across machine learning biomarker models, highlighting a five-domain framework required for safe clinical implementation.

Key Points

To synthesize the evidence needed to translate machine learning-based biomarker models into clinical decision-support systems and establish a structured readiness framework.
Conducted a structured narrative literature search across PubMed, Scopus, Web of Science, and Google Scholar focusing on biomarker validation, calibration, workflow integration, and governance.
Qualitatively mapped 70 core publications across five readiness domains: methodological, clinical, operational, governance, and post-deployment.
Identified pervasive translational deficiencies in published models, notably poor calibration, lack of external validation, unstable features, workflow mismatch, and absence of post-deployment monitoring for drift.
Formulated a stage-gated, five-domain translational-readiness framework that delineates computational predictive performance from operational readiness and accounts for modality-specific data vulnerabilities.

Cite This Study

Vahid Habibzadehomran (2026) studied this question.

synapsesocial.com/papers/6a93f0e06c1a8fb52e79d74e https://doi.org/10.1007/s44163-026-02035-z

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Implication

Narrative review demonstrates widespread translational deficits across machine learning biomarker models, highlighting a five-domain framework required for safe clinical implementation.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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