Impact Factor
4.54
Journal Citation Impact
Estimated influence, citation density, publishing volume, and citation integrity from the analytics table.
Citation rate
16.38
2,573 total citations
h5-index
56
h5 depth signal
Cited share
82.7%
139 of 168
Total citations
2,573
Citation footprint
Metric signal
9.99
External metric anchor
Self-citation
1.8%
47 self-citations
Citations Trend
Monthly Publications Cadence
Citing Geographic Demographics
United States
268
China
38
Germany
36
United Kingdom
29
Canada
27
Citing Fields of Research
AI & Machine Learning
295
Brain Imaging
205
Biomedical Physics
168
Computer Vision & Graphics
150
Breast Cancer
68
Publishing Activity & Reach
Volume, recency, access pattern, and citing-community signals from the journal analytics record.
All-time article footprint
634
Articles tracked across the journal lifetime
Indexed publication set
168
139 cited publications
Last 12 months
112
14.0 publications/month
Open access share
3.6%
4 OA publications in the last year
Leading citing country
United States
268 citing publications
Leading citing field
AI & Machine Learning
295 citing publications
Latest tracked publication
1 November 2026
10 recent records available
Coverage & Indexing History
v5n1, Jan. 2023-
v1n1, Jan. 2019-
Active Coverage
Research Focus & Classification
Indexed Subjects
Brain Imaging
AI & Machine Learning
Breast Cancer
Biomedical Physics
Lung Cancer
Issuing & Sponsoring Bodies
issuing body
Radiological Society of North America
Broad Categories
Diagnostic Imaging
Radiology
Brain Imaging
Biomedical Physics
Breast Cancer
Lung Cancer
Prostate Cancer
MeSH Descriptors
Artificial Intelligence
Diagnostic Imaging
Radiology
Latest Publications
Recent article records with DOI-first linking and condensed abstracts.
01
DOI 10.1148/ryai.260801
Open DOI
From Per-Examination Savings to Project-level Breakeven in Expert-guided Segmentation
02
DOI 10.1148/ryai.260515
Open DOI
Comparative Performance of Federated and Centralized Learning for Brain Tumor Classification and Segmentation
Purpose To compare federated learning (FL) and centralized learning (CL) performance for brain tumor classification and segmentation and examine whether specific configurations confer measurable performance advantages. Materials and Meth...
03
DOI 10.1148/ryai.251134
Open DOI
ARNAI: Artifact Removal Network Based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
Purpose To develop an artificial intelligence framework, robust to spinal implants, for automated measurement of spinopelvic parameters on postoperative radiographs. Materials and Methods Lateral lumbar spine radiographs from two institu...
04
DOI 10.1148/ryai.260835
Open DOI
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): Explanation, Elaboration, and Examples
The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) provides a structured framework for transparent and reproducible reporting of AI studies in medical imaging. Since its introduction in 2020, CLAIM has been widely adopt...
05
DOI 10.1148/ryai.260131
Open DOI
Deep Learning Analysis of Dual-Modality US Videos for the Characterization of Superficial Lymphadenopathy
Purpose To develop and validate a deep learning (DL) model based on dual-modality US videos to differentiate benign from malignant superficial lymphadenopathy (LA) and stratify malignant subtypes. Materials and Methods This multicenter s...
06
DOI 10.1148/ryai.250941
Open DOI
A Deep Learning Model to Predict Breast Cancer Recurrence Using Longitudinal Mammograms and Clinical Data
Purpose To develop a deep learning model that integrates longitudinal mammograms and static clinical data for predicting the recurrence risk and recurrence subtype of breast cancer. Materials and Methods In this retrospective study, the ...
07
DOI 10.1148/ryai.250630
Open DOI
DeepVEST: Deep Learning-based Vessel Segmentation and Erasure in Breast MRI for Improved Lesion Assessment
Purpose To investigate whether selective removal of vascular structures can improve lesion visibility and interpretability in maximum intensity projection (MIP) images derived from dynamic contrast-enhanced MRI. Materials and Methods A r...
08
DOI 10.1148/ryai.260811
Open DOI
Assuring Transparency of AI Software after Deployment
09
DOI 10.1148/ryai.260325
Open DOI
Human-AI Collaboration in Radiology: The Blind Spots
Despite rapid technological advances in radiology, radiologist–artificial intelligence interactions remain critically understudied. Three specific domains—cognitive psychology, user interface and user experience, and algorithmic conformi...
10
DOI 10.1148/ryai.260100
Open DOI
Image Quality in the Era of Artificial Intelligence: Understanding the Limitations of AI-based Image Reconstruction and Postprocessing
Artificial intelligence (AI)–based image reconstruction and postprocessing may produce images that appear visually superior but cannot add patient-specific diagnostic information; understanding this limitation is essential for the inform...
Bibliographical & Catalog Notes
Complemented by (work): Radiology 0033-8419 (DLC) 24011915 (OCoLC)1763380 (DNLM)0401260.
Complemented by (work): Radiology. Cardiothoracic imaging 2638-6135 (DLC) 2018202925 (OCoLC)1048897843 (DNLM)101748663.
Complemented by (work): Radiology. Imaging cancer 2638-616X (DLC) 2018202926 (OCoLC)1048898015 (DNLM)101765309.