ArticleResearch square2025
A Systematic Review and Implementation Guidelines of Multimodal Foundation Models in Medical Imaging.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 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
6 authors.
Funding
Abstract
Artificial Intelligence (AI) holds immense potential to transform healthcare, yet progress is often hindered by the reliance on large labeled datasets and unimodal data. Multimodal Foundation Models (FMs), particularly those leveraging Self-Supervised Learning (SSL) on multimodal data, offer a paradigm shift towards label-efficient, holistic patient modeling. However, the rapid emergence of these complex models has created a fragmented landscape. Here, we provide a systematic review of multimodal FMs for medical imaging applications. Through rigorous screening of 1,144 publications (2012-2024) and in-depth analysis of 48 studies, we establish a unified terminology and comprehensively assess the current state-of-the-art. Our review aggregates current knowledge, critically identifies key limitations and underexplored opportunities, and culminates in actionable guidelines for researchers, clinicians, developers, and policymakers. This work provides a crucial roadmap to navigate and accelerate the responsible development and clinical translation of next-generation multimodal AI in healthcare.
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.