Clinically Meaningful Multimodality in Medical AI : Complementarity, Redundancy, and Conflict Across Clinical Data Streams

Abstract

Multimodal artificial intelligence (AI) increasingly integrates imaging, pathology, omics, electronic health records, clinical text, and physiological signals. However, multimodality is often defined by the number of input types, leaving unclear when additional data improve a clinical decision, duplicate available evidence, or introduce informative disagreement. This concept‐driven critical perspective proposes a relationship‐centered framework for clinically meaningful multimodality. Drawing on a focused synthesis of recent PubMed literature and foundational studies, we define complementarity, redundancy, and conflict as context‐dependent relationships shaped by the clinical question, decision point, timing, and available evidence. We examine how attention mechanisms, gating mechanisms, graph structures, mixture‐of‐experts models, and modular encoders can represent or regulate these relationships. We then organize the framework across five dimensions linking modality relationships to clinical meaning, representative contexts, modeling strategies, and evaluation approaches. Relationship‐centered design principles are further connected to a staged evaluation pathway spanning representation‐level validation, relationship‐aware technical validation, clinical‐utility assessment, prospective implementation, and monitoring. Pulmonary nodule assessment, reperfusion decision‐making in acute ischemic stroke, and immunotherapy response assessment illustrate how the same modality may shift between complementary, redundant, and conflicting roles across clinical workflows. We argue that multimodal systems should be evaluated not only by discrimination, but also by marginal modality value, calibration, reduced‐input robustness, behavior under missing, asynchronous, or discordant evidence, and effects on clinical decisions. Future research should prioritize relationship‐preserving representations, decision‐aware modality acquisition, relationship‐specific benchmarks, and human‐centered evaluation. Clinically meaningful multimodality depends not on how many data streams a model can fuse, but on whether it uses each stream for a defined clinical purpose while preserving evidence provenance, timing, and uncertainty.