ReviewDiagnostic and interventional radiology (Ankara, Turkey)2025
The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review
Review in Diagnostic and interventional radiology (Ankara, Turkey), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
What it found
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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
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers.Journal of clinical medicine · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.Journal of imaging · 2026Review
- Diagnostic Performance of Contemporary Large Language Models on Free-Text Histopathologic Descriptions in Oral and Maxillofacial Pathology.Head and neck pathology · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Article
- A Pilot Study Protocol for AI-Assisted Interpretation of Chest X-rays for Pulmonary Abnormalities in Uganda.Cureus · 2026Article
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction.NPJ digital medicine · 2026Article
- Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine.PLoS computational biology · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.medRxiv : the preprint server for health sciences · 2026Article
- Meta learning optimized TabNet for small sample repeat prostate biopsy prediction.Discover oncology · 2026Article
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- AVPENet: Pain estimation from audio-visual fusion of non-speech sounds.PLOS digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiology is more pronounced than ever. An increasing number of technical and clinical AI-focused studies are published each day. As these tools inevitably affect patient care and physician practices, it is crucial that radiologists become more familiar with the leading strategies and underlying principles of AI. Multimodal AI models can combine both imaging and clinical metadata and are quickly becoming a popular approach that is being integrated into the medical ecosystem. This narrative review covers major concepts of multimodal AI through the lens of recent literature. We discuss emerging frameworks, including graph neural networks, which allow for explicit learning from non-Euclidean relationships, and transformers, which allow for parallel computation that scales, highlighting existing literature and advocating for a focus on emerging architectures. We also identify key pitfalls in current studies, including issues with taxonomy, data scarcity, and bias. By informing radiologists and biomedical AI experts about existing practices and challenges, we hope to guide the next wave of imaging-based multimodal AI research.
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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.