ReviewCommunications engineering2026
Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.
Review in Communications engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Microfluidic Light-Scattering Imaging Coupled with Deep Learning for Label-Free Single-Cell Classification of Lymphoma Cells.Biosensors · 2026Article
- Review
- Multimodal imaging and AI in brain tumor surgery: current tools and emerging integration.npj biomedical innovations · 2026Review
- From Adaptive Resilience to Catastrophic Systems Collapse: Endothelial Entropy, Ferroptotic Propagation, and the Maternal Point of No Return in Emergency Peripartum Hysterectomy.International journal of molecular sciences · 2026Review
- Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.NPJ digital medicine · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.