Authors: Khot Shruti Rahul, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Clinical diagnosis rarely relies on one source of information: doctors combine medical images, symptoms described by patients, clinical reports, laboratory results, electronic health records, and sometimes audio or video such as heart sounds or gait recordings. Multimodal Artificial Intelligence integrates such heterogeneous data to improve the accuracy, efficiency and reliability of diagnosis. This paper reviews multimodal AI for intelligent healthcare diagnosis assistance. It introduces the taxonomy of multimodal machine learning, covering representation, alignment and fusion, and compares early, intermediate and late fusion strategies. Applications are reviewed in disease detection, image interpretation with reports, personalized treatment and patient monitoring, together with vision–language models that learn from paired images and text and generalist medical foundation models that accept many input types. Public multimodal datasets are discussed. A five-layer architecture comprising multimodal data acquisition, modality-specific encoders, fusion, diagnostic reasoning and clinician-facing explanation is proposed, with research gaps, objectives, a comparison of fusion strategies and challenges including missing modalities, data alignment, privacy, interpretability and validation. The review concludes that multimodal AI can provide more comprehensive patient analysis and faster decisions when it is rigorously validated and clinically supervised.
DOI: https://doi.org/10.5281/zenodo.24034883
