Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation

Abstract

Multimodal artificial intelligence (AI) technologies are transforming medical practices by integrating diverse data sources to enable more accurate diagnosis, disease prediction, and treatment planning. In this review, we explore state-of-the-art multimodal AI systems, focusing on their applications in clinical settings, including radiology, pathology, and clinical imaging, as well as non-image data, such as electronic health records (EHRs) and multi-omics data. We highlight how combining multiple modalities improves diagnostic accuracy and prognostic prediction compared to unimodal models. The study emphasizes the importance of robust data fusion strategies and model interpretability for real-world clinical deployment. By addressing key challenges, such as data heterogeneity and uncertainty quantification, this research offers a new paradigm for intelligent healthcare. The findings suggest that the continued advancement of multimodal AI will significantly enhance clinical decision-making, paving the way for personalized medicine and improved patient outcomes.

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