ReviewFrontiers in medicine2025
Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Multimodal deep learning outperforms clinical and brain region models in predicting stroke-associated pneumonia: an explainable AI study.Frontiers in neurology · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 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
No grant is acknowledged in the PubMed record.
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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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.