A scoping review of explainable artificial intelligence for medical multimodal data.
TL;DR
Suggestions are provided for the field to transition from individual and post-hoc XAIs toward intrinsically explainable designs where the reasoning logic is built directly into the model architecture to ensure that AI outputs align with human-centric clinical workflows and applications.
Abstract
Multimodal Artificial Intelligence (AI) models-integrating diverse data such as imaging and clinical records-are advancing rapidly in healthcare, yet a significant disconnection persists between these complex predictive architectures and the explainable AI (XAI) techniques used to interpret them. We conducted a scoping review over 4 bibliographic databases to investigate the use of explainability methods in cross-modal medical AI studies. From 82 included studies, we found that the landscape remains dominated by independent feature attribution (assigning importance scores to individual modality in isolation), with the majority of studies relying on post-hoc methods (applied after a model decision is reached) that treat the model as a 'black box'. While emerging trends like visual grounding (linking textual justifications directly to specific image regions) and model reasoning show promise, a critical gap remains in explaining the underlying reasoning process. Standardised evaluation is missing in the majority of studies relying solely on qualitative measures. Only a minority of studies achieve good reproducibility with public codebase. We provide suggestions for the field to transition from individual and post-hoc XAIs toward intrinsically explainable designs where the reasoning logic is built directly into the model architecture to ensure that AI outputs align with human-centric clinical workflows and applications.