ReviewBiomedical engineering letters2025
Vision-language foundation models for medical imaging: a review of current practices and innovations.
Review in Biomedical engineering letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
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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.
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Who cites it
21 citing papers in PubMed.
- End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Comparison of Multimodal Large Language Models and Oral and Maxillofacial Radiologists in the Detection of Incidental Findings on Panoramic Radiographs: A CBCT-Referenced Diagnostic Accuracy Study.Healthcare (Basel, Switzerland) · 2026Article
- Large Intestine Model Editor (LIME): Design and pilot evaluation of an editor for parameterized large intestine 3D models.Medical & biological engineering & computing · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.Abdominal radiology (New York) · 2026Review
- Reply to Sridharan K et al.Pediatric radiology · 2026Article
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Handcrafted Versus Deep Feature Extraction Methods for MRI-Based Multiple Sclerosis Diagnosis.Diagnostics (Basel, Switzerland) · 2026Article
- Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.Journal of medical Internet research · 2026Article
- Validation of a Dermatology-Focused Multimodal Image-and-Data Assistant in Diagnosis and Management of Common Dermatologic Conditions.Medicina (Kaunas, Lithuania) · 2026Article
- Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Text-image alignment for ILD imaging: linking CXR evidence to CT quantification.NPJ digital medicine · 2026Article
- A Comparative Assessment of ChatGPT, Gemini, and DeepSeek Accuracy: Examining Visual Medical Assessment in Internal Medicine Cases with and Without Clinical Context.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative Evaluation of Vision-Language Models for Detecting and Localizing Dental Lesions from Intraoral Images.Journal of imaging · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- Standardizing DICOM annotation: deep learning enhances body part description in X-ray image retrieval for clinical research.BMC medical imaging · 2025Article
- An Empirical Evaluation of Low-Rank Adapted Vision-Language Models for Radiology Image Captioning.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Foundation models, including large language models and vision-language models (VLMs), have revolutionized artificial intelligence by enabling efficient, scalable, and multimodal learning across diverse applications. By leveraging advancements in self-supervised and semi-supervised learning, these models integrate computer vision and natural language processing to address complex tasks, such as disease classification, segmentation, cross-modal retrieval, and automated report generation. Their ability to pretrain on vast, uncurated datasets minimizes reliance on annotated data while improving generalization and adaptability for a wide range of downstream tasks. In the medical domain, foundation models address critical challenges by combining the information from various medical imaging modalities with textual data from radiology reports and clinical notes. This integration has enabled the development of tools that streamline diagnostic workflows, enhance accuracy (ACC), and enable robust decision-making. This review provides a systematic examination of the recent advancements in medical VLMs from 2022 to 2024, focusing on modality-specific approaches and tailored applications in medical imaging. The key contributions include the creation of a structured taxonomy to categorize existing models, an in-depth analysis of datasets essential for training and evaluation, and a review of practical applications. This review also addresses ongoing challenges and proposes future directions for enhancing the accessibility and impact of foundation models in healthcare. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-025-00484-6.
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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.