Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", or mentions of writing academic reviews on deep learning for medical imaging.
npxskills add luwill/research-skills--skill medical-imaging-reviewLoading…
Produce comprehensive reviews that pass first-round peer review on factual grounds, not just structural grounds.
This is not a template-filling skill. It is a write-with-verify discipline.
First choose the review type. Read references/REVIEW_TYPES.md before collecting literature or drafting prose.
Default narrative/method-survey projects live in 4 files:
project_root/
├── PARADIGM.md # Style spec from 2-3 exemplar reviews (Phase 0)
├── CLAUDE.md # Project-specific terminology + literature inventory
├── IMPLEMENTATION_PLAN.md # 3-axis outline + per-claim verification checklist
└── manuscript_draft.md # The actual manuscript
Scoping and systematic reviews add protocol, search, screening, extraction, and risk-of-bias files; see references/REVIEW_TYPES.md and references/REPORTING_STANDARDS.md.
Follow the workflow in references/WORKFLOW.md. The phases are: review-type routing -> paradigm capture -> init -> collect-and-verify -> outline/taxonomy -> write-with-per-claim-verification -> peer review -> submission prep.
Do not let the title outrun the methods.
| If the user asks for... | Route to... | Read |
|---|---|---|
| flagship "综述", narrative synthesis, method survey | Narrative review / method survey | REVIEW_TYPES.md, DOMAINS.md |
| evidence map, "what exists", gap mapping | Scoping review | REVIEW_TYPES.md, REPORTING_STANDARDS.md |
| systematic review, meta-analysis, diagnostic-accuracy evidence | Systematic review route | REVIEW_TYPES.md, REPORTING_STANDARDS.md |
If the manuscript uses the phrase "systematic review", it must contain reproducible search strings, eligibility criteria, screening flow, extraction fields, and risk-of-bias methods. Otherwise, call it a narrative review, method survey, or scoping review.
Calibrate language to evidence strength, not to a fixed hedging register.
When ≥2 independent peer-reviewed groups confirm a finding, state it strongly. When evidence is single-source or contested, state it cautiously. When evidence is absent, say so.
Avoid the LLM tells:
These phrases are AI-detector top features. Real flagship-review authors don't use them. Strip them.
Take a position when evidence supports it. Neutral catalogue is the LLM default and the failure mode to avoid. See Verdict sentences below.
Every [N] citation must satisfy these checks (the full 5-rule protocol is in references/CITATION_INTEGRITY.md):
If any check fails, the citation is broken — fix before continuing. This is a hard gate: even a single broken citation must be fixed before delivery.
Do not fill in a template like [Author] et al. [ref] proposed [method]... Achieves Dice of X.XX. That template is a hallucination trap.
Use this discipline instead:
If you can't access the paper, do not write about its internal architecture or specific performance numbers — and do not assert priority or novelty ("first to", "首个", "earliest", "novel"). Priority claims are strong, falsifiable, and frequently wrong; asserting one you haven't verified is a hallucination. Instead, cite it for a neutral, non-priority contribution ("applied X to Y") and, if useful, note the claim is unverified — or leave it out.
##) for top-level sections (Introduction, Methods, Applications, Discussion, ...).###) for subsections.**Topic.** paragraph starters for deeper grouping.1., 1.1, 1.2.3) unless the target journal explicitly requires numbered sections.Display equations (DSC, IoU, clDice, FedAvg, GCN propagation, ...) appear in Boxes, not inline in body paragraphs. Textbook formulas can be referenced ("the Dice similarity coefficient — see Box 1") but should not be displayed inline.
If a formula has no methodological insight worth displaying (e.g., FedAvg averaging), describe it in prose instead of showing it.
Vendor names (HeartFlow, Cleerly, Caristo, Keya, Shukun, ...) belong primarily in the Commercial Products / Regulatory & Validation table. In body text use category descriptors unless the product name is necessary to define a regulatory fact, trial population, or head-to-head distinction.
Reason: repeated product names in body text read like marketing copy. Use exact product names when precision matters; cite peer-reviewed evidence for clinical claims.
# [Title]: <evocative subtitle>
## Key Points
- 4-5 bullets, each 1-3 sentences, expressing the main conclusions.
## Abstract
## Introduction
### Clinical background
### Technical challenge
### Scope and contributions
## Datasets and evaluation metrics
(Table 1: public datasets)
(Box 1: evaluation metrics with equations)
## Methods # 3-axis grouping is the default for method surveys
### Architectural priors
**CNN-based design.** ... (bold lead-in for sub-grouping)
**Transformer-based design.** ...
**Mamba and state-space design.** ...
### Inductive priors
**Topology-aware design.** ...
**Multi-task design.** ...
**Graph-based design.** ...
### Data regime
**Self-supervised pre-training.** ...
**Foundation models.** ...
**Federated learning.** ...
**Physics-informed models.** ...
(Table 2: representative methods with modality / family / dataset / metric)
## Downstream applications
### [Application 1]
### [Application 2]
### [Application 3]
## Translation to clinical practice
(Table 3: commercial products with regulatory + validation)
## Outstanding challenges
## Future directions
## References
Notes:
For narrative reviews and method surveys, each major method-axis subsection (Architectural priors / Inductive priors / Data regime) should close with one verdict sentence expressing authorial position. Choose the 3-5 most opinionated positions across the whole manuscript — don't put verdicts on every paragraph.
For systematic and scoping reviews, verdicts must be constrained by the protocol and evidence map. Prefer "the included studies show..." over broad field-wide claims unless the search was designed to support the broader claim.
Verdict templates:
Neutral catalogue is the LLM default and exactly what flagship review editors push back on. Force yourself to take 3-5 positions.
scripts/audit_manuscript.py before delivery (resolve the path relative to this skill directory). The script is a triage tool — it flags likely issues from surface patterns; it does not prove any citation or number is correct. Delivery requires both a clean script pass (0 critical/high) and a manual source-level spot-check of quantitative and directional claims. A green script alone is not sufficient.Full rationale is in Core Principles above; this is the at-a-glance recap.
**Topic.**; systematic/scoping Methods may follow PRISMA/journal conventions. (details)$$…$$) live in Box 1 (rarely additional Boxes); textbook formulas with no methodological insight go in prose, not inline. (details)# Data citation
"...achieved Dice of 0.730 on ImageCAS [N]"
# Method citation
"Xu et al. [N] introduced..."
# Multi-citation (max 4 in one bracket — beyond that, regroup the claim)
"Multiple groups demonstrated this effect [N1, N2, N3]"
# Comparative
"While [N1] focused on architecture, [N2] addressed the data side"
[N] in body must match the bibliography entry [N], and bibliography [N] must be the paper the body sentence is actually attributing the claim to. See references/CITATION_INTEGRITY.md Rule 3.
Use source types in combination. Confirm which tools are available in the current environment before using tool-specific names.
| Source | Best for | Preferred route | Fallback |
|---|---|---|---|
| ArXiv | Methodological preprints, ML/AI advances | Available arXiv MCP or paper search | arXiv abstract/PDF URLs |
| PubMed | Peer-reviewed clinical / validation studies | PubMed MCP or NCBI/PubMed search | PubMed URL by PMID |
| Zotero | User's local library (closed-access journals) | Available Zotero MCP or local Zotero API | user-provided PDFs |
| Crossref | DOI verification | Crossref API/WebFetch | DOI resolver and publisher page |
| Local PDFs | Exemplar reviews and closed-access papers | PDF text extraction | visual/manual reading |
For closed-access journals (Med Image Anal, Eur Radiol, Lancet family) the user's local Zotero library is often the only path. Always check Zotero before assuming a paper is inaccessible.
For tool-adapter guidance, see references/MCP_SETUP.md.
| File | Read when |
|---|---|
| references/REVIEW_TYPES.md | Before starting — choose narrative, scoping, systematic, meta-analysis, or umbrella route |
| references/REPORTING_STANDARDS.md | Whenever the manuscript claims systematic/scoping methods or appraises AI studies |
| references/WORKFLOW.md | Starting a new review or moving between phases |
| references/PARADIGM.md | Phase 0: capturing exemplar review style spec |
| references/CITATION_INTEGRITY.md | Phase 2 (collection) and Phase 4 (write) — every citation must follow the 5 rules |
| references/HALLUCINATION_PATTERNS.md | Phase 4 (write) and Phase 5 (peer review) — checklist of 10 LLM hallucination indicators to self-check against |
| references/DOMAINS.md | Phase 3 (outline) — 3-axis method groupings per domain |
| references/TEMPLATES.md | Phase 1 (init) — CLAUDE.md, IMPLEMENTATION_PLAN.md, table templates |
For revising an existing AI-drafted review (whether your own previous output or someone else's draft), use ai-review-revision if it is installed. That skill is the dedicated tool for fixing draft-quality issues — multi-agent diagnostic, factual reset, structural reset, content polish, submission prep.
This skill (medical-imaging-review) is the dedicated tool for producing draft-quality content correctly the first time. They are complementary:
If a draft produced by this skill still ends up needing the ai-review-revision workflow to land, that's a bug — flag it so this skill can be improved.
v3.0.0 was rewritten after the coronary-cta-paper draft exposed recurring failure modes: placeholder DOIs, citation drift, fabricated method modules, wrong performance numbers, vendor-style citations, flat method taxonomy, and AI-tone hedging.
v3.1.0 adds review-type routing, reporting-standard guidance, tool portability, softer structure rules, CCTA terminology correction, and an executable manuscript audit script.
v3.2.0 hardens the guardrails: the audit script now detects author↔citation mismatches under standard "Author et al. [N]" typesetting and recognises internationalised reference headings (## 参考文献, etc.) so Chinese drafts no longer mis-flag every citation; a fixture test suite (scripts/tests/) locks these in. Hard factual errors are now zero-tolerance (not gated behind a "5-or-more" threshold), unverified priority/novelty claims are forbidden, Phase 5 peer review is rewritten as executable sub-agent passes, and DOMAINS.md gains a generative/multimodal (VLM, diffusion, promptable-segmentation) paradigm section.
Consolidated fix ledger (v3.0.0 → v3.2.0):
| Earlier failure | Current fix |
|---|---|
| Hedging mandate; 80-120 reference target | Removed — match voice to evidence; cite what supports the argument |
| Method fill-in template; flat 10-subsection taxonomy | Read-first/write-after discipline; 3-axis grouping default |
| Structural-only QA; no source verification | Per-claim verification (Phase 4) + CITATION_INTEGRITY 5 rules + HALLUCINATION_PATTERNS |
| Systematic label without methods; hard-coded MCP names | Review-type routing (PRISMA/QUADAS/CLAIM/TRIPOD) + tool-adapter fallbacks |
| Numbered headings; scattered vendors; inline equations; neutral catalogue | Bold lead-ins; Table-3-first; Box-1 equations; 3-5 required verdicts; Phase 0 PARADIGM |
| Audit gate ineffective on standard/Chinese citations | Marker-anchored author check + i18n reference headings + fixture tests (v3.2.0) |
| Before delivering a draft to the user |
| references/MCP_SETUP.md | Tool adapters and fallbacks for arXiv / PubMed / Zotero / Crossref |
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