We propose Retrieval-Augmented Diagnosis (RAD), a holistic method that explicitly integrates disease-centered medical knowledge into multimodal diagnosis models.

A contrastive learning framework integrating discrepancy estimation and adaptive attention for medical time-series diagnosis.