Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› radiology
radiology
GitHub辅助评估医学影像研究是否适合投稿至Radiology期刊，涵盖诊断准确性、AI方法学、STARD/CLAIM报告规范及拒稿风险，提供选题与重构建议。
Trigger Scenarios
作者指定Radiology为目标期刊需进行匹配度检查
需要将影像研究围绕临床意义和参考标准重新构思
在Radiology与其他期刊间做选择决策
需要确认诊断准确性或可重复性的报告期望
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill radiology -g -y
SKILL.md
Frontmatter
{
"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.mdand../../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-imagingin 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>
Version History
- 1839142 Current 2026-07-05 12:36
