Radiology journals cover diagnostic performance, imaging biomarkers, interventional procedures, nuclear medicine, radiation science, workflow, and rapidly expanding artificial-intelligence research. Modality, anatomy, clinical use case, and validation level are central to finding the right readership.
This guide ranks journals associated with radiology, imaging, nuclear medicine, and ultrasound classifications. For AI manuscripts in particular, authors should also compare external validation, reporting standards, dataset transparency, and clinical utility requirements.
About this ranking
Rankings use the Journal Metrics Score, computed from OpenAlex (CC0) data; it is not the Journal Impact Factor. Scores update with each data snapshot and are only one part of journal selection. This page uses the current Journal Metrics dataset and links every title to its detailed record. Confirm current metrics, scope, indexing, access fees, and author instructions before submission.
Top radiology journals by Journal Metrics Score
Compare the leading matched journals, then open the individual record for more detail.
How to choose a radiology journal
Scope before score
Confirm that the journal regularly publishes your condition, method, population, and article type before comparing metrics.
Match the reader
Choose the clinicians or researchers who should use the finding. A focused audience can be more valuable than a broader but less relevant one.
Check the evidence tier
Large trials, definitive cohorts, technical notes, case series, and laboratory studies often belong in different journals within the same specialty.
Verify current policies
Review indexing, open-access options, fees, reporting checklists, data policies, and author instructions on the journal's own website.
Major radiology publishing areas
Diagnostic radiology
Modality-based and organ-based diagnostic studies, imaging biomarkers, and clinical interpretation.
Interventional radiology
Image-guided procedures, devices, technical outcomes, safety, and comparative treatment studies.
Nuclear medicine and molecular imaging
Tracer development, PET and SPECT imaging, theranostics, and quantitative molecular imaging.
Imaging informatics and AI
Algorithms, workflow tools, radiomics, validation studies, and clinical implementation of imaging software.
Continue your journal search
Use the specialty ranking as a shortlist, then compare exact journal records and broader medical publishing guidance.
Frequently asked questions
How are the best radiology journals ranked on this page?
The table starts with journals matched to radiology topics in the Journal Metrics dataset, removes duplicate ISSNs, excludes records without a usable Journal Metrics Score, and orders the remaining journals by that score, which is computed from OpenAlex (CC0) data. It is a metric-led shortlist, not a universal judgment of research quality or manuscript fit.
What is a useful Journal Metrics Score for a radiology journal?
A useful Journal Metrics Score depends on the specialty and article type. Compare journals within the same OpenAlex subfield and quartile rather than applying one threshold across medicine. Scope, readership, indexing, editorial standards, and access options remain important.
How should I choose among top radiology journals?
Start with scope and audience, then compare recent papers with your disease area, study design, population, and article type. Use the Journal Metrics Score and OpenAlex subfield quartile as citation context, but confirm current author instructions and publication policies on the journal website.
What should an imaging AI paper demonstrate before targeting a leading radiology journal?
Strong candidates usually provide clinically relevant endpoints, appropriate comparator performance, transparent cohorts, external or temporal validation, and a clear account of how the system would change radiology workflow or patient care.
Written by Dr. Meng Zhao
Physician-Scientist · Founder, LabCat AI
MD · Former Neurosurgeon · Medical AI Researcher
Dr. Meng Zhao is a former neurosurgeon turned medical-AI researcher. After years in the operating room, he moved into applied AI for clinical workflows and now leads LabCat AI, a medical-AI company working on decision support and research tooling for clinicians. He built Journal Metrics as a free resource for researchers who need reliable journal metrics without paid database subscriptions.
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