Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality, yet remains substantially underdiagnosed, as single-modal artificial intelligence (AI) captures only a fraction of its biological complexity. Multimodal deep learning (DL) offers a promising route to improved diagnosis, severity classification, exacerbation prediction, and personalized management, but no prior synthesis is architecturally grounded across the COPD clinical continuum.
