radiology

name	radiology
description	Use when targeting Radiology (RSNA) or deciding whether a medical-imaging study fits this venue. Encodes the journal's fit, the diagnostic-accuracy and imaging-methodology bar, STARD/CLAIM reporting and reproducibility expectations, RSNA house style, official-submission re-check, and desk-reject heuristics. Venue-fit aid only, not clinical advice.

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.

Official-submission checklist

• Before giving submission-ready advice, read ../../resources/source-basis.md and ../../resources/official-source-map.md; start from the ICMJE/EQUATOR and RSNA anchors, then cite the current Radiology page you checked.
• Search the live site for "Radiology RSNA instructions for authors" and follow the current version.
• Re-check article types, abstract/summary format, and word/figure limits.
• Confirm the STARD (diagnostic) or CLAIM (AI) checklist, and prospective registration where the study design requires it.
• Re-check IRB/ethics and consent, patient-image de-identification and consent, ICMJE authorship and conflict-of-interest disclosure, funding, data/code availability, and AI-use disclosure.
• If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

• The study asks a clinically meaningful imaging question with a valid, independent reference standard.
• Sample size/power is justified; reader studies report inter-/intra-reader agreement.
• AI/ML work separates train/validation/test data and includes external/multi-site validation (CLAIM).
• Diagnostic-accuracy reporting follows STARD with a flow diagram; spectrum/verification bias addressed.
• Images are de-identified, high-quality, and annotated; acquisition parameters reported.
• IRB/consent, disclosures, and a data/code-availability statement are prepared.

Common desk-reject triggers

• Small, single-center retrospective series with no reference-standard rigor or limited generalizability.
• AI models evaluated only on internal data, with no external validation or clinical baseline.
• Diagnostic-accuracy studies with verification or spectrum bias and no STARD reporting.
• Quantitative-imaging claims with no repeatability/reproducibility evidence.
• Pure technical/phantom work with no clinical relevance, better suited to a physics or subspecialty journal.

Re-routing decision

• Subspecialty imaging focus (neuro/cardiac/abdominal) → a dedicated subspecialty imaging journal.
• Imaging-AI methodological advance over clinical validation → a medical-imaging methods venue (e.g., ieee-transactions-on-medical-imaging in the engineering bundle).
• Cardiology/neurology clinical outcome dominant over imaging method → jama-cardiology / jama-neurology / stroke.
• Oncology imaging with a clinical-oncology endpoint → jama-oncology / annals-of-oncology.
• Broad, practice-changing significance → general medicine (jama / NEJM in the natural-science bundle).

Output format

[Fit] High / Medium / Low (one-line reason)
[Target] Radiology (RSNA)
[Imaging tags] <modality + task, e.g. MRI diagnostic accuracy, imaging AI>
[Design / reporting guideline] <diagnostic-STARD / AI-CLAIM / interventional-outcome>
[Method/evidence] <reference standard, sample size, external validation>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / STARD or CLAIM / registration / de-identification / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>