Motivation
Multimodal Artificial Intelligence (AI) is an emerging field in high-performance computational sciences that integrates multiple data streams—such as text, images, videos, audio, and numerical data—to enhance information extraction, inference accuracy, and bias reduction. By synthesizing diverse data sources, multimodal AI provides a more comprehensive representation of complex physical, medical, and societal processes.
In mission-critical domains like healthcare, multimodal AI has the potential to revolutionize medical analytics, improving disease detection, prediction, diagnosis, risk stratification, referrals, and clinical decision-making. As modern healthcare systems generate vast and diverse datasets—including medical reports, clinical notes, radiology images, physician dictations, patient audio recordings, physiological signals, and genomic data — AI models must effectively process and integrate these different data types, simulating how the human brain synthesizes multiple sensory inputs for decision-making.
Despite its advantages, multimodal AI faces challenges in data alignment across modalities, requiring well-annotated datasets and advanced embedding techniques. Additionally, integrating domain-specific medical knowledge is crucial for clinically meaningful interpretations.
This Workshop will showcase the latest advancements in multimodal AI-driven biomedical research and healthcare applications, addressing key challenges, emerging solutions, and future opportunities.