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name: academic-aio description: Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist. metadata: triggers: "AIO, LLMO, GEO, AI search optimization, discoverability, abstract optimization, structured abstract, Key Points, Research in context, plain-language summary, preprint strategy, GitHub README, CITATION.cff, Zenodo DOI, Hugging Face model card, dataset card, Perplexity, Elicit, Consensus, SciSpace, RAG visibility, reporting guideline compliance, TRIPOD-AI, CLAIM, STARD-AI, taxonomy review paper, Radiology Key Points, Lancet Digital Health Research in context, npj Digital Medicine"

Academic AIO Skill — Medical AI Paper Visibility for AI Search Engines

Communication Rules

• Surface the checklist in the response. Never apply AIO edits silently.
• When a rule conflicts with journal formatting, defer to the journal and mark the item NA with the reason.
• When introducing a rule, cite its source (TRIPOD+AI, CLAIM, STARD-AI, Wu 2025, Algaba 2024, Aggarwal 2024 GEO) by DOI or arXiv ID from External References. Never invent a citation, DOI, arXiv ID, or reporting-guideline item number; mark anything you cannot verify [VERIFY].

Section 1 — Title and Abstract Optimization

1.1 Title three-slot rule

Structure: [Task] + [Modality or anatomy] + [Model family or method class]. Include one concrete differentiator (dataset scale, new benchmark, "first …") when defensible. Avoid keyword stuffing (penalized as spam by AI overviews).

• PASS: "Transformer-based segmentation of skull fractures on non-contrast head CT."
• FAIL: "A novel advanced deep-learning AI machine-learning framework for medical image analysis."

1.2 Structured abstract

Use the journal-required structure (Background / Methods / Findings / Interpretation for the Lancet family; Background / Purpose / Materials and Methods / Results / Conclusion for the RSNA family; etc.). If the journal allows unstructured, still structure it internally. Each section stands alone as a semantic chunk of ≤ 3 sentences so that RAG chunk-boundary splits do not break the claim.

1.3 Opening and closing sentences

• First sentence: the problem AND the contribution in one line. Never open with filler ("In recent years, AI has revolutionized …") — it burns the chunk LLM summarizers extract most.
• Last sentence: explicit interpretation ("we show that …", "this implies …"). No hedging-only closes.

1.4 Taxonomy line

Include one sentence that names the field's controlled vocabulary ("diagnostic-accuracy study", "foundation-model evaluation", "LLM-as-judge", "agentic radiology workflow"). Entity linkers in AI indexes use this line.

1.5 Quantified claim

Every abstract must contain at least one numeric primary outcome with a confidence interval (for example, "AUC 0.94 [95 % CI 0.91–0.96]").

1.6 Reporting-guideline anchor

Name the guideline in the abstract or the opening sentence of Methods: "Reported following TRIPOD+AI (Collins 2024) and CLAIM 2024 (Tejani 2024)". Add STARD-AI 2025, DECIDE-AI, or TRIPOD-LLM when applicable. AIO-rule ↔ guideline-item mapping: references/reporting_guideline_mapping.md.

1.7 Keyword, MeSH, and RadLex coverage

Title, abstract, and keywords together should cover the concept's key terms, without repeating title terms in the keywords (92 % of the surveyed papers repeated key terms across title, abstract and keywords; Royal Society 2024, doi:10.1098/rspb.2024.1222). Include:

• Core MeSH terms (verify against the NLM MeSH browser) and RadLex terms where applicable.
• Modality synonyms ("chest radiograph (CXR)", "non-contrast CT (NCCT)").
• Both US and UK spellings when relevant.

Section 2 — Manuscript-Level AIO

2.1 Summary box

Include the journal-specific summary box verbatim when supported; it is the fragment AI search engines most often copy or paraphrase:

• Lancet family: "Research in context" (Evidence before this study / Added value / Implications).
• RSNA Radiology and RYAI: 3 bullets, one claim each — labelled "Key Results" for Radiology and "Key Points" for RYAI in references/journal_summarybox_templates.yaml. Confirm the label in the current RSNA instructions for authors; the format check accepts either label for Radiology.
• npj Digital Medicine: no summary box; Articles carry an unstructured abstract of up to 150 words.
• Nature Medicine: editor's summary (supplied by editorial, but draft one proactively).

Journal-specific templates: references/journal_summarybox_templates.yaml. Never invent a summary-box rule: verify each template against the journal's current instructions for authors before applying it.

Deterministic format check. Validate the drafted box against its journal spec with python3 ${CLAUDE_SKILL_DIR}/scripts/check_summary_box.py --manuscript <file> --journal <stem> --strict (reads references/summary_box_specs.json: Key Points / Key Results top-level bullet count + one-claim-per-bullet, Research-in-context's three sub-blocks each opening a line and carrying text, plain-language word band). It catches the wrong-format / wrong-bullet-count box that a production technical check rejects.

Known limits. The box ends only at a heading or a bold-only label line, so a list under a plain-text label (Abbreviations:) right after the box is counted into it, and an emphasized body phrase that starts with the box label (*Key points* of prior work ...) is taken as the box; read the box by eye when either shape is present. In a Research-in-context box, a bold-only line after the last sub-block label ends the box, so a sub-heading inside Implications (**For clinicians**) cannot be told from the next section; an Implications sub-block left empty that way is reported as an advisory, not a failure.

2.2 Declarative section headings

Headings state a claim, not a generic label: "Model underperforms on rare-finding subset" beats "Subgroup analysis".

2.3 Numeric claim compression

In the Methods and in at least one Results paragraph, compress primary-outcome statistics into one sentence: "On the internal test set (n = 842), the model achieved AUC 0.94 (95 % CI 0.91–0.96), sensitivity 88.2 % (85.1–91.0), specificity 91.4 % (88.7–93.6), at an operating point of 0.37."

2.4 Reproducibility block

Include a labeled block (end of Methods or a standalone Data/Code Availability section) listing data availability and license, code availability with DOI, model weights and checkpoints, prompts and configuration files, random seeds, and compute environment. Flag code described only as "available on reasonable request": scrapers read it as not reproducible and AI tools demote the paper.

2.4a Citing an AI-assisted tool by use-class

Frame an AI-assisted tool by what it did, not by hiding it: verification/QA and analysis uses go in a Software / Code-availability statement (citable); generative drafting or humanizing goes in the journal's AI-use disclosure field, not a citation. A self-citation by the tool's author also requires a COI disclosure and should cite only the functions the work used. Use-class table and rules: ${CLAUDE_SKILL_DIR}/references/ai_tool_citation_framing.md.

2.5 Limitations enumeration

List limitations explicitly and name each one (generalizability, spectrum bias, dataset shift, single-center training, label noise). Flag "clinical grade" or "replaces radiologists" overclaims: LLM trust heuristics demote them and they invite reviewer rejection.

2.6 Standalone figure captions

Each caption re-states the claim, the dataset, and the metric, because captions survive in vector and image-retrieval indexes when the body text is lost.

Section 3 — Preprint, Channel, and Indexing Strategy

3.1 Preprint versus fast-track

• Default: post to medRxiv (clinical), arXiv (methods, cs.CV / eess.IV), or bioRxiv on the day of journal submission. This puts the paper into Semantic Scholar within 24–72 hours and into Perplexity's web index immediately.
• Exception: if the target journal offers a fast-track review cycle (acceptance → online within roughly 30–60 days) AND the authors prefer a single canonical version, the preprint may be skipped; compensate with aggressive post-acceptance SNS seeding and PMC deposit.
• Never skip both preprint and fast-track — a paywall-only paper is invisible to most RAG pipelines.

3.2 Journal preprint policy

Most medical-AI venues allow preprints; a few restrict them or require disclosure. Verify the current policy on Sherpa Romeo or the journal's instructions for authors before posting.

3.3 Indexing time-lag

Read ${CLAUDE_SKILL_DIR}/references/launch_sequencing.md when planning launch timing; it lists how long each index takes to pick a paper up.

3.4 Open-access choice

Prefer gold OA with CC-BY when budget allows; otherwise green OA via preprint plus author-accepted manuscript. Closed-access papers without a preprint lose roughly 30–50 % of AI-tool citations because Elicit, Consensus, and Perplexity Academic cannot extract from paywalled PDFs.

Funder OA-policy decision tree (Plan S, NIH, UKRI, Gates, Wellcome, NRF, MoHW): references/oac_funding_checklist.yaml.

3.5 Post-acceptance channel checklist

• Deposit AAM to PMC or Europe PMC.
• Update ORCID and Google Scholar profile.
• Post to Threads / X / BlueSky with DOI, one-sentence claim, and figure; long-form post on LinkedIn.
• Submit to Papers with Code if the paper reports a benchmark.
• Upload model or dataset to Hugging Face with a model/dataset card.

Section 12 gives the day-by-day order.

Section 4 — Review-Paper Strategy

Read ${CLAUDE_SKILL_DIR}/references/pre_draft_strategy.md when the artifact is a review or taxonomy paper, or the phase is pre-draft.

Section 5 — GitHub, CITATION.cff, Zenodo, Hugging Face

Read ${CLAUDE_SKILL_DIR}/references/repository_and_cards.md when the artifact is a README, CITATION.cff, Zenodo record, or Hugging Face model/dataset card (rules 5.1–5.6).

• Embed JSON-LD ScholarlyArticle / SoftwareSourceCode / Dataset / Person markup in repository pages and author landing pages — templates in references/schema_markup_templates/, validated with python scripts/validate_schema.py path/to/file.jsonld.
• Never auto-complete author lists, ORCIDs, or affiliations in CITATION.cff or Zenodo metadata; surface the empty slots to the user.

Section 6 — Authority and E-E-A-T Signals

• Maintain an author landing page (GitHub Pages, personal domain, or institutional page) listing all papers with DOIs and open-access links.
• Use one consistent affiliation string across papers; inconsistency fragments the author entity in knowledge graphs.
• Keep ORCID complete and linked to Google Scholar. Re-run author disambiguation on Semantic Scholar every 6 months.
• Cross-link related papers by the same group in Discussion sections when defensible; within-group citation raises co-retrieval in RAG.
• Refresh repository and model cards quarterly; quarterly-updated articles outperform single-publish ones in AI-overview retention (Conductor 2026 benchmark).

Section 7 — LLM-Citation Fabrication Defense

Between 50 % and 90 % of LLM answers to medical questions are not fully supported by the sources they cite (Wu et al., Nat Commun 2025, doi:10.1038/s41467-025-58551-6). Defend the paper's identifiers:

• Surface DOI and PMID in copy-friendly text at the top of the paper's landing page and README (for example, DOI: 10.xxxx/yyyy • PMID: 12345678).
• Add a "How to cite" section with BibTeX, APA, Vancouver, and the plain-text line in one place.
• Monitor incorrect citations: a Google Scholar alert for the paper's title variant, plus periodic Perplexity and ChatGPT web queries that record hallucinated bibliographic errors. Report discoverability (Perplexity / Elicit / Consensus retrieval) only from a recorded probe; never estimate it.
• When a reviewer cites an LLM-generated reference, verify the DOI and PMID yourself before accepting it.

Section 9 — Per-Project Application and Pipeline Integration

When invoked, run in this order:

1. Read the target artifact (title, abstract, manuscript section, README, or card) and identify its lifecycle phase: pre-draft / drafting / pre-submission / post-acceptance / post-publication.
2. Apply Sections 1–5 and 10 relevant to that artifact, filtering each rule by its applies_to_phase field in references/checklists/AIO_GENERAL.md. Out-of-phase rules become NA rather than FAIL (e.g., do not surface §11.5 multi-disciplinary roster or §12 launch sequencing as FAIL on a pre-submission audit). Produce a PASS / PARTIAL / FAIL table, one-line reason and concrete fix per item, sorted by expected_lift (high → medium → low), in the output template of references/checklists/AIO_GENERAL.md. Render via templates/aio_audit_checklist.md.j2 when programmatic.
3. Honour defers_to annotations to avoid duplicate audits. Items annotated with a defers_to field record only present/absent status here; item-level detail belongs to the linked skill or reference (§1.6 → /check-reporting; §3.4 / §11.3 → references/oac_funding_checklist.yaml). When the manuscript has not had a reporting-guideline audit, invoke /check-reporting first; the AIO ↔ guideline-item mapping is in references/reporting_guideline_mapping.md.
4. Apply the Section 6 author-authority audit once per submission cycle, and Sections 11–12 as the applies_to_phase filter allows.
5. Surface Section 7 citation-defense recommendations at post-acceptance time. For multi-repo or Hugging-Face-card team audits, run scripts/batch_metadata_audit.py.
6. Output:
   • The phase-filtered checklist (visible).
   • A short deferred-item list with one-line status per defers_to rule.
   • At most 5 concrete edits ranked by expected_lift (high first, then medium, then low). A low-lift edit enters the Top 5 only when no high / medium item remains open.

Integration with other skills

• Run after /self-review and /humanize, so QC-confirmed claims and the final human-readable text anchor the checklist.
• Within /write-paper: apply Section 1 while drafting the title and abstract, Sections 2.5 and 6 (cross-linking) while drafting the Discussion, and the full checklist at QC, after the reporting-guideline check and numerical-claim audit.

Section 10 — Q&A and Entity-Extraction Optimization

10.1 Four-question Q&A block (Discussion or Appendix)

Add a labeled Q&A block, either as the closing subsection of Discussion or as a Supplementary Box:

• What was known before this study? — two-sentence restatement of the prior state.
• What does this study add? — two-sentence statement of the contribution.
• How might this change clinical practice or research? — one-sentence interpretation; avoid overclaim.
• Why does this matter? — one-sentence "so what" framing for non-specialists.

Lancet Digital Health "Research in context" already encodes the first two questions; the block extends them.

10.2 Glossary block with entity IDs

Define each domain-specific acronym inline on first use AND list them in a Glossary subsection at the end of Methods or in the Supplement, with the canonical entity ID where possible: MeSH term ID (clinical concepts), RadLex ID (radiology terms), UMLS CUI (cross-vocabulary mapping), Hugging Face model ID (named models), arXiv ID (cited methods).

10.3 Inline citation anchor text

Avoid bare reference numbers; bind each citation to its specific claim:

• WEAK: "Prior work [12] showed efficacy."
• STRONG: "Smith et al. (DOI: 10.xxxx/yyyy) reported a 12 % accuracy gain on the MIMIC-CXR test set [12]."

When citing one's own prior work, name the cohort or dataset explicitly to enable cross-paper retrieval.

10.4 Explicit challenge statement

Beyond the 2.5 limitations list, include a single-paragraph "Why this is hard" statement near the start of Discussion:

"Building accurate [task] for [modality/anatomy] is constrained by [data scarcity / label noise / dataset shift / regulatory uncertainty / interpretability]. Each of these has been documented [refs], and our results address [subset]."

LLM web-search systems quote challenge statements as authoritative summaries of field state (worked case: references/case_studies/kjr_mllm_2025.md).

Section 11 — First-Mover Timing and Citation-Graph Density

Rules 11.1 (topic-peak detection), 11.2 (editorial-board leverage), and 11.5 (multi-disciplinary author roster) are in ${CLAUDE_SKILL_DIR}/references/pre_draft_strategy.md; read it at pre-draft.

11.3 PMC-auto-deposit journal preference

OA journals that auto-deposit to PubMed Central reach LLM crawlers within 4–6 weeks of publication; non-PMC OA journals can take 3–6 months. When all else is equal, prefer a PMC-auto-deposit journal. Radiology/medical-AI examples (verify per submission; policies change):

• Korean Journal of Radiology (KJR) — auto-deposit confirmed.
• Lancet Digital Health — author-funded green OA, PMC-eligible after embargo.
• Radiology and Radiology: AI — selected articles auto-deposit.
• npj Digital Medicine — auto-deposit (Nature OA).
• JAMIA — author-funded OA route.
• JMIR — auto-deposit (PMC-indexed).

11.4 Citation-graph anchor strategy

Anchor the Discussion in 5–10 high-visibility prior works that LLM training corpora already index well, so the paper is retrieved when users query those works.

• Identify seminal references via Semantic Scholar's "Highly Influential Citations" filter for the topic.
• Cite them with semantic predicates (Section 10.3), not as bare lists.
• Mix recent preprints (currency) with 2018–2022 seminal papers (graph anchoring); a 2024–2025-only citation profile has low LLM retrieval weight because of corpus cutoffs.

Section 12 — Cross-Platform Launch Sequencing

At post-acceptance / post-publication, read ${CLAUDE_SKILL_DIR}/references/launch_sequencing.md for the Day 0 → Month 1 sequence (rules 12.1–12.5).

External References

• GEO: Generative Engine Optimization — Aggarwal et al., KDD 2024, arXiv:2311.09735.
• LLM medical citation support — Wu et al., Nat Commun 2025, doi:10.1038/s41467-025-58551-6.
• LLM citation bias — Algaba et al., 2024, arXiv:2405.15739.
• ExpertQA attribution — Malaviya et al., 2024, arXiv:2309.07852.
• TRIPOD+AI — Collins et al., BMJ 2024. EQUATOR Network.
• CLAIM 2024 — Tejani et al., Radiology: AI 2024, doi:10.1148/ryai.240300.
• STARD-AI — Sounderajah et al., Nat Med 2025, doi:10.1038/s41591-025-03953-8.
• TRIPOD-LLM — Gallifant et al., Nat Med 2024, doi:10.1038/s41591-024-03425-5.
• DECIDE-AI — Vasey et al., Nat Med 2022, doi:10.1038/s41591-022-01772-9.
• Title, abstract, keywords guide — Royal Society Proc B 2024, doi:10.1098/rspb.2024.1222.
• GitHub repository citation advantage — Kang et al., Inf Process Manag 2023, doi:10.1016/j.ipm.2023.103477.
• Semantic Scholar Open Data Platform — Kinney et al., arXiv:2301.10140.