ReviewVisual computing for industry, biomedicine, and art2023
Vision transformer architecture and applications in digital health: a tutorial and survey.
Review in Visual computing for industry, biomedicine, and art, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.BMC ophthalmology · 2025Pooled it
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Vision transformer autoencoders captures local and non-local features in brain imaging to reveal novel genetic associations.Communications biology · 2026Article
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs.Scientific reports · 2026Article
- TongueNet-GYN: a multimodal deep learning framework for non-invasive gynecological disease screening in digital public health.Frontiers in public health · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification.Frontiers in medicine · 2026Article
- Clinical Application of Vision Transformers for Melanoma Classification: A Multi-Dataset Evaluation Study.Cancers · 2025Article
- Acute myeloid leukemia classification using ReLViT and detection with YOLO enhanced by adversarial networks on bone marrow images.Scientific reports · 2025Article
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- Vision Transformer Autoencoders for Unsupervised Representation Learning: Revealing Novel Genetic Associations through Learned Sparse Attention Patterns.medRxiv : the preprint server for health sciences · 2025Article
- Intelligent quality assessment of ultrasound images for fetal nuchal translucency measurement during the first trimester of pregnancy based on deep learning models.BMC pregnancy and childbirth · 2025Article
- Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images.Scientific reports · 2025Article
- Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.Journal of dental sciences · 2025Article
- Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data.Frontiers in bioengineering and biotechnology · 2025Article
- Research on grading detection methods for diabetic retinopathy based on deep learning.Pakistan journal of medical sciences · 2025Article
- Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection.Frontiers in big data · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Fetal Diagnostics using Vision Transformer for Enhanced Health and Severity Prediction in Ultrasound ImagingCurrent medical imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth such as report generation and security. This article also presents a roadmap for implementing the ViT in digital health systems and discusses its limitations and challenges.
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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.