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@article{203829,
        author = {YAMINI A C and Dr.K.Priya},
        title = {A Comprehensive Review on Handling Multimodal Data Using Machine Learning Techniques in Healthcare Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2862-2870},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203829},
        abstract = {The integration of multimodal data in healthcare systems constitutes one of the most consequential frontiers in contemporary medical informatics. This paper presents a systematic review of frameworks and methodologies developed for handling multimodal healthcare data using Machine Learning (ML) and Deep Learning (DL) techniques, spanning literature from 2020 to 2024. With the proliferation of advanced data acquisition technologies, healthcare environments now generate diverse modalities—Electronic Health Records (EHRs), medical imaging (CT, MRI, PET), genomic sequences, wearable sensor streams, and patient-generated health data—whose convergence holds transformative potential for diagnostic accuracy and personalized treatment. In this work, numerous related frameworks are studied across five thematic clusters: multimodal fusion architectures, federated learning, vision-language foundation models, cancer prognosis with multi-omics, and EHR-driven representation learning. Empirical results consistently demonstrate that multimodal approaches outperform unimodal baselines. It also enumerates open challenges in data fusion, privacy-preserving integration, missing-modality robustness, and interpretability, and propose concrete future research directions toward clinically deployable multimodal AI systems.},
        keywords = {Multimodal data, healthcare systems, data fusion, federated learning, vision-language models, EHR, genomics, deep learning, convolutional neural network, transformer, patient outcomes.},
        month = {June},
        }