Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging
Abstract
The rapid evolution of AI has facilitated innovative solutions in analysing different biomedical imaging modalities. By leveraging the complementary information from each modality, multimodal AI solutions have shown a huge potential to go beyond human capabilities and offer advances in bioimaging. At the same time, new foundation models and transformer-based architectures are now poised to address unsolved challenges in this field. This review aims to explore and discuss the state-of-the-art AI techniques applied in multimodal biomedical imaging, presenting the key challenges and future directions. We discuss several integration strategies to combine multiple biomedical imaging data types. We also focus on methods to overcome the open challenges related to data quality, model interpretability, and ethical implications. The rapid evolution of AI has facilitated innovative solutions in analysing different biomedical imaging modalities. This review outlines state-of-the-art methods, key challenges, and future directions for using AI in multimodal biomedical imaging.
Authors: Le Minh Thao Doan, Kaveh Shahhosseini, Suraj Verma, Abdolreza Marefat, Giorgio Locicero, Sneha Verma, Claudio Angione, Annalisa Occhipinti
Published in: Communications Engineering (2026)