Sasi Kumar Kolla Examines Multimodal Foundation Models for Precision Medicine Research
Healthcare data is generated across many forms, including genomic sequences, medical images, clinical notes, and laboratory records, yet most artificial intelligence systems in medicine are still built to interpret only one of these formats at a time. Researcher Sasi Kumar Kolla has taken up this gap in a new paper that examines how foundation deep learning models can be designed to work across multiple data types at once, rather than being confined to a single modality.
His paper, titled Foundation Deep Learning Models For Precision Medicine Using Multimodal Big Data, published in the International Journal of Advances in Signal and Image Sciences, lays out a framework for how genomics, transcriptomics, radiology images, and electronic health records might be brought together within a single modeling approach, and what data infrastructure would need to exist to support that kind of integration.
Why Single-Modality Models Fall Short
According to Kolla, most existing...
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