A scoping review of explainable artificial intelligence for medical multimodal data.
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.