Keywords
Modality Risk
Expert Routing
Missing Data
Calibration
Clinical Safety
Abstract
Trustworthy Multimodal Biomedical Ai is increasingly shaped by the need to reconcile performance with evidence quality, resource limits, and transfer across settings. The present synthesis investigates routing modalities according to conditional reliability rather than assuming every signal is always useful. The source set brings together 1 focal paper with 13 independently retrieved publications validated against DOI-registration metadata. The analysis is organized around modality risk, expert routing, missing data, calibration, and clinical safety. The analysis declines to treat results from heterogeneous studies as exchangeable, the review compares problem definitions, methodological assumptions, and validation boundaries. Across the literature, the comparison suggests that advances in trustworthy multimodal biomedical AI become credible when measurement, model, and clinical decision are evaluated together and when uncertainty about calibration is reported explicitly. The comparative structure connects method selection to decision consequence and recurring validity threats, and proposes a research agenda centered on well-specified controls, robustness tests, and reproducible workflows.
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