Radiology

Radiology (radiology)

Journal positioning

Radiology is the flagship journal of the Radiological Society of North America (RSNA), publishing original research across diagnostic and interventional imaging — imaging physics and technique, diagnostic accuracy, image-guided intervention, and imaging artificial intelligence — with a strong emphasis on rigorous design, adequate sample size, and clinical relevance. The defining expectation is a methodologically sound imaging study with a clinically meaningful question and an appropriate reference standard, not a small retrospective series or an AI model evaluated on a single internal dataset. This skill is a fit / venue-selection / re-framing aid; it is not clinical or regulatory advice and does not replace the journal's current instructions. Before submitting, re-check the live Radiology author instructions.

When to trigger

• The author names Radiology for a diagnostic-imaging, imaging-physics, interventional, or imaging-AI study and wants a fit/framing check.
• An imaging study must be re-framed around a clinically meaningful diagnostic or outcome question with a valid reference standard.
• The author is choosing between Radiology, a subspecialty imaging journal, and a general clinical journal.
• The author needs the journal's diagnostic-accuracy reporting and reproducibility expectations (STARD, CLAIM for AI).

Scope & topic fit

• Diagnostic-accuracy studies across modalities (CT, MRI, ultrasound, PET, radiography) with an appropriate reference standard.
• Imaging physics, acquisition, reconstruction, and quantitative-imaging biomarker development and validation.
• Image-guided and interventional procedures with outcome data.
• Artificial intelligence and machine learning for imaging, with rigorous training/ validation/test design and external validation.
• Prognostic and screening imaging studies with clinically meaningful endpoints.

Method & evidence bar

• Diagnostic-accuracy studies need an adequate, representative sample, a valid and independent reference standard, and reporting per STARD; spectrum and verification bias must be addressed.
• Sample size and statistical power must be justified; reader studies require adequate readers and inter-/intra-reader agreement analysis.
• AI/ML studies require clearly separated training/validation/test data, external/ multi-site validation, and reporting per CLAIM; performance must be benchmarked against a clinically relevant baseline (e.g., radiologists or standard of care).
• Quantitative-imaging claims need repeatability/reproducibility evidence and, where relevant, multi-vendor/multi-site generalizability.
• Retrospective designs must address selection bias and confounding; prospective and multi-center evidence strengthens fit.

Structure & house style

• RSNA format with a structured abstract and a short "key results" / summary statement; re-check current article types (Original Research, etc.) and limits on the live guide.
• A STARD (or CLAIM for AI) flow diagram and completed checklist are expected where applicable.
• Figures are central and must be high-quality, de-identified images with clear annotations; report acquisition parameters.
• Methods must give enough acquisition, analysis, and (for AI) model and data detail to allow reproduction; data/code sharing strengthens the submission.