ReviewJournal of imaging informatics in medicine2025
Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.
Review in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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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Who cites it
31 citing papers in PubMed.
- Cardiovascular Disease Risk Assessment from Retinal Fundus Images Using a CSP-ConvNext Model.Diagnostics (Basel, Switzerland) · 2026Article
- GSTP1 expression mitigates endothelial dysfunction in diabetic retinopathy through regulation of oxidative stress: insights from single-cell RNA sequencing.Acta diabetologica · 2026Article
- Article
- Multimodal transformers predict cancer therapy response from tumor mechanics.Array (New York, N.Y.) · 2026Article
- Enhancing Digital Breast Tomosynthesis Sinograms via Budget-Constrained PSO-Nelder-Mead: A Vision Transformer Assessment.Biomimetics (Basel, Switzerland) · 2026Article
- Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast.Npj imaging · 2026Review
- Deep learning model based on MRI-derived microvascular network simulation parameters for noninvasive assessment of lymphovascular invasion in rectal cancer patients.Journal of gastrointestinal oncology · 2026Article
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- AI-Enhanced Prognostic Model for Predicting Polyp Recurrence and Guiding Post-Polypectomy Surveillance Intervals Using the ERCPMP-V5 Dataset.Journal of clinical medicine · 2026Article
- Review
- Efficient SqueezeViT: A lightweight vision transformer framework for chest X-ray image classification.Scientific reports · 2026Article
- GastroMalign: Vision Transformer-Based Framework for Early Detection and Malignancy-Risk Stratification for High-Risk Gastrointestinal Lesions.Journal of clinical medicine · 2026Article
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- Maxillary sinus classification for sex and age using 23 artificial intelligence architectures.Scientific reports · 2026Article
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- A survey of transformer-based architectures in medical image analysis: models, applications, and challenges.Frontiers in artificial intelligence · 2026Review
- Transformers meet CNNs for insights into breast mass classification from histopathological images.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of artificial intelligence techniques, particularly deep learning, has transformed medical imaging. This paper presents a comprehensive review of recent research that leverage vision transformer (ViT) models for medical image classification across various disciplines. The medical fields of focus include breast cancer, skin lesions, magnetic resonance imaging brain tumors, lung diseases, retinal and eye analysis, COVID-19, heart diseases, colon cancer, brain disorders, diabetic retinopathy, skin diseases, kidney diseases, lymph node diseases, and bone analysis. Each work is critically analyzed and interpreted with respect to its performance, data preprocessing methodologies, model architecture, transfer learning techniques, model interpretability, and identified challenges. Our findings suggest that ViT shows promising results in the medical imaging domain, often outperforming traditional convolutional neural networks (CNN). A comprehensive overview is presented in the form of figures and tables summarizing the key findings from each field. This paper provides critical insights into the current state of medical image classification using ViT and highlights potential future directions for this rapidly evolving research area.
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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.