(IMAGE)
Caption
Cardiovascular care generates inherently multimodal data that are frequently incomplete, asynchronously acquired, and dynamically altered by clinical events and treatment. A clinically meaningful patient representation should therefore capture not only what was observed, but also the reliability and timing of each observation, patterns and reasons for missingness, and changes in clinical state over time. Multimodal information can then be encoded and interpreted within its temporal and clinical context. Before a prediction is issued, evidence sufficiency should be assessed to determine whether the available information supports prediction. When prespecified criteria are met, the model may provide clinical decision support; when evidence is insufficient, it should defer to an alternative pathway such as clinician review, additional testing, or usual care. Performance, calibration, model-derived uncertainty, clinical utility, deferral behaviour, generalisability, and equity should be evaluated separately across relevant care settings and patient populations. The lower panel summarises six principles for trustworthy multimodal cardiovascular AI: representing the patient rather than the dataset; treating missingness as clinically meaningful; respecting temporal and clinical context; assessing evidence sufficiency and supporting deferral; validating generalisability and equity; and aligning model outputs with clinical decision-making and workflow.
Credit
Ge Zhang / Medicine Discovery
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License
CC BY-NC