ReviewJournal of magnetic resonance imaging : JMRI2019
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.
Review in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 205 papers, 2 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
205 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.International journal of colorectal disease · 2025Pooled it
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- Deep Learning Based on Automated Breast Volume Scanner Images for the Diagnosis of Breast Lesions: A Multicenter Diagnostic Study.International journal of medical sciences · 2025Trial
- MRI of Total Hip Arthroplasty: Successful Ripple Artifact Reduction Using Wavelet Domain Filtering.Journal of magnetic resonance imaging : JMRI · 2026Article
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Research Activity and Subspecialty Imbalance of Artificial Intelligence in Orthopaedics: A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Deep learning for automated detection and classification of temporomandibular joint disorders: A scoping review.Imaging science in dentistry · 2026Review
- Benefits of high-magnification and color information in computational pathology-An exploratory review.Journal of pathology informatics · 2026Review
- Enhanced ovarian cancer diagnosis using deep learning on pelvic ultrasound with integrated clinical data: retrospective multicenter study.Journal of gynecologic oncology · 2026Article
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Multi-modal personalized federated learning with adaptive differential privacy for medical image classification and a privacy-preserving approach.Scientific reports · 2026Article
- Deep learning-based vessel segmentation in non-contrast black-blood MRI for treated aneurysm follow-up: a comparative study with TOF-MRA and DSA.Neuroradiology · 2026Article
- Physics-based simulation of ultrasound propagation from MRI volumes for cross-modal breast imaging.Journal of biological engineering · 2026Article
- Equity and Generalizability of Radiomics in Orbital Disease: Challenges for Ophthalmology, Otolaryngology, and Plastic Surgery.Diagnostics (Basel, Switzerland) · 2026Review
- Teeth identification and numbering in mixed dentition: evaluating deep learning models for pediatric panoramic radiographs.BMC oral health · 2026Article
- Exploring AI in radiology: a bibliometric study tailored to a research path.Radiologie (Heidelberg, Germany) · 2026Article
- Advances and challenges in AI-assisted MRI for lumbar disc degeneration detection and classification.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Artificial intelligence in early onset scoliosis: a scoping review.Spine deformity · 2026Article
145 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
4 authors.
Funding
Abstract
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.
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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.