/academic-aio

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 h