ReviewJapanese journal of radiology2019
Technical and clinical overview of deep learning in radiology.
Review in Japanese journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05633732 (Echocardiography Image Quality Management System Based on Deep Learning), which is not on this map. Cited by 59 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.
Echocardiography Image Quality Management System Based on Deep Learning: A Single-center Prospective Study
Who cites it
59 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.AJNR. American journal of neuroradiology · 2024Pooled it
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Legal liability of physicians and new governance in the AI era.Japanese journal of radiology · 2026Article
- AI for screening in healthcare: promise and challenges.Abdominal radiology (New York) · 2026Review
- Deep learning models for radiography body-part classification and chest radiograph projection/orientation classification: a multi-institutional study.European radiology · 2026Article
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- FAD-MIL: a weakly supervised fracture detection model based on X-ray images.Scientific reports · 2026Article
- Artificial Intelligence-Based Evaluation of Permanent First Molar Extraction Indications in Children Using Panoramic Radiographs.Children (Basel, Switzerland) · 2026Article
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Observational
- Development and validation of a deep learning model for severe mitral stenosis detection from chest X-rays.Open heart · 2025Article
- Deep learning for appendicitis: development of a three-dimensional localization model on CT.Japanese journal of radiology · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- The critical need for an open medical imaging database in Japan: implications for global health and AI development.Japanese journal of radiology · 2025Article
- ChatGPT's diagnostic performance based on textual vs. visual information compared to radiologists' diagnostic performance in musculoskeletal radiology.European radiology · 2025Article
- Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.Clinical neuroradiology · 2024Article
- Deep learning of echocardiography distinguishes between presence and absence of late gadolinium enhancement on cardiac magnetic resonance in patients with hypertrophic cardiomyopathy.Echo research and practice · 2024Article
- Data set terminology of deep learning in medicine: a historical review and recommendation.Japanese journal of radiology · 2024Review
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep learning, deep learning techniques are divided into five categories: classification, object detection, semantic segmentation, image processing, and natural language processing. After a brief overview of technical network evolutions, clinical applications based on deep learning are introduced. The clinical applications are then summarized to reveal the features of deep learning, which are highly dependent on training and test datasets. The core technology in deep learning is developed by image classification tasks. In the medical field, radiologists are specialists in such tasks. Using clinical applications based on deep learning would, therefore, be expected to contribute to substantial improvements in radiology. By gaining a better understanding of the features of deep learning, radiologists could be expected to lead medical development.
Indexed as
Identifiers
30506448What 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.