Machine learning (ML) and multi-modal artificial intelligence (AI) promise transformative improvements in healthcare: earlier diagnosis, personalized treatment, and system-level efficiency. Yet these promises are coupled with well-documented risks algorithmic bias, opacity, and fragile generalization that can exacerbate health inequities and undermine trust. This paper proposes a principled, research-grade framework for trustworthy multi-modal AI in healthcare that integrates (1) systematic bias detection across modalities (imaging, structured EHR, clinical text, genomics), (2) causal and statistical explanation tools, and (3) layered mitigation strategies (preprocessing, in-training constraints, post-hoc adjustments, and socio-technical governance). We ground the framework in recent empirical failures and methodological advances, present an extensible software + evaluation pipeline, specify datasets and metrics for rigorous benchmarking (including subgroup calibration and counterfactual tests), and describe deployment-level governance aligned with WHO, EU and clinical reporting standards. Implementation recommendations emphasize reproducible code, federated/private training options, adversarial robustness checks, and continuous monitoring. Throughout we highlight tradeoffs (fairness definitions, utility vs. parity, explainability vs. fidelity) and provide concrete protocols researchers and health systems can adopt to reduce harms and make multi-modal AI clinically trustworthy.
Daniel R. Anderson (Sun,) studied this question.