ReviewMedical image analysis2023
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.
Review in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 3 of them syntheses that pooled it.
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
113 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- From Patient Selection to Surveillance: Artificial Intelligence Applications in Radioiodine Therapy - A Systematic Review.Nuclear medicine and molecular imaging · 2026Review
- Transformer-Based PET/CT Fusion Enables Preoperative Prediction and Prognostic Stratification of Spread Through Air Spaces in Lung Adenocarcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SApneaNet: Adaptive Squeeze-and-Excitation-Based CNN-Transformer Network with AGFF for Sleep Apnea Event Detection Using ECG Images Under IoMT.Sensors (Basel, Switzerland) · 2026Article
- Demographic Reporting and the Absence of Hispanic/Latino Representation in Dermatology Artificial Intelligence: A Scoping Review.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Article
- Downstream-Aware Automated QC of Images and AI-Generated Segmentations.Journal of imaging informatics in medicine · 2026Article
- Fully Automated Segmentation of Anatomical Subregions of the Thoracolumbar Spine on Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- STL-DeepBDC: A Robust Few-Shot Learning Framework for Multiclass Ovarian Tumor Classification in Ultrasound Outperforms Conventional Transfer Learning.Cancer medicine · 2026Article
- PAT-Mamba: Adaptive all-in-one image restoration for photoacoustic tomography via state space model.Photoacoustics · 2026Article
- Frequency-domain multi-scale hybrid attention for pathological image classification.Scientific reports · 2026Article
- Review
- Impact of data augmentation size on deep learning-based third lumbar vertebra computed tomography skeletal muscle segmentation performance.Quantitative imaging in medicine and surgery · 2026Article
- A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.Scientific reports · 2026Article
- Global spatiotemporal biomechanics using video swin transformer: multiscale validation and clinical impact for keratoconus suspects.NPJ digital medicine · 2026Article
- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- Article
- Application of transformer models in medical image segmentation: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- AI Augmented Confocal Laser Endomicroscopy for Rapid Intraoperative Diagnosis of Brain Tumors.NPJ digital medicine · 2026Article
53 more citing papers are in PubMed but not listed here.
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
Transformer, one of the latest technological advances of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance to computer vision, it is natural to inquire about the status quo of Transformers in medical imaging and ask the question: can the Transformer models transform medical imaging? In this paper, we attempt to make a response to the inquiry. After a brief introduction of the fundamentals of Transformers, especially in comparison with convolutional neural networks (CNNs), and highlighting key defining properties that characterize the Transformers, we offer a comprehensive review of the state-of-the-art Transformer-based approaches for medical imaging and exhibit current research progresses made in the areas of medical image segmentation, recognition, detection, registration, reconstruction, enhancement, etc. In particular, what distinguishes our review lies in its organization based on the Transformer's key defining properties, which are mostly derived from comparing the Transformer and CNN, and its type of architecture, which specifies the manner in which the Transformer and CNN are combined, all helping the readers to best understand the rationale behind the reviewed approaches. We conclude with discussions of future perspectives.
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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.