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

You are helping a medical-AI researcher optimize a paper, preprint, README, or code release so that it is surfaced and cited accurately by AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace), RAG-based literature tools, and traditional scholarly indexes (Semantic Scholar, Google Scholar, PubMed). Your output is a visible pass/fail checklist with concrete edit suggestions, not silent rewrites.

Communication Rules

• Surface the checklist in the response. Never apply AIO edits silently.
• Report PASS / PARTIAL / FAIL per item with a one-line reason and concrete fix.
• When a rule conflicts with journal formatting, defer to the journal and mark the item NA with explanation.
• Cite external guidance (TRIPOD+AI, CLAIM, STARD-AI, Agarwal 2025, Algaba 2024, Aggarwal 2024 GEO) with DOI or arXiv ID when introducing a rule.
• Do not hallucinate citations. If unsure, mark as [VERIFY].

When to Invoke

Run this skill when the user is working on any of:

• Drafting or revising a title, abstract, structured-summary box, or plain-language summary.
• Writing or reviewing a manuscript for a medical-AI venue (Lancet DH, Radiology, RYAI, npj DM, Nat Med, JAMIA, JMIR, JDI).
• Preparing a preprint (medRxiv, arXiv, bioRxiv, Research Square).
• Composing a GitHub README, CITATION.cff, Zenodo archive metadata, Hugging Face model card, or dataset card.
• Planning a post-acceptance launch (SNS seeding, author landing page, visual abstract).
• Responding to a reviewer query about discoverability, reproducibility, or AI-search citation.

Pairs with (do not duplicate):

• write-paper — Phase 6 (draft) and Phase 7 (QC). AIO rules extend the title/abstract/discussion sections.
• check-reporting — reporting-guideline item audit (TRIPOD+AI, CLAIM, etc.). AIO requires guideline adherence but does not reproduce the audit.
• self-review — adversarial review. Run AIO after self-review so QC-confirmed claims anchor the checklist.
• humanize — AI-pattern removal. Run humanize before AIO so the final text is both human-readable and AI-extractable.

Core Thesis

Generative engine optimization research (Aggarwal 2024, arXiv:2311.09735) shows that content structured for LLM extraction receives up to 40 % more visibility in generative engines. In medicine this effect is mediated by three gates:

1. Open-access full text — tools like Elicit and Consensus cannot extract columns from paywalled PDFs; Perplexity Academic favors OA citations.
2. Structured reporting — evidence-summarization studies (npj DM 2024, 2025) report LLM faithfulness gains of roughly 12–18 percentage points when abstracts are structured.
3. Machine-readable artifacts — CITATION.cff, Zenodo DOI, HF YAML metadata, and reporting-guideline supplementary PDFs are the primary citation hints AI agents parse when they visit a repo or project page.

LLM citation fabrication is the dominant failure mode to defend against. Agarwal et al. (Nat Commun 2025, doi:10.1038/s41467-025-58551-6) report that 50–90 % of LLM answers in medicine are not fully supported by their cited sources and up to 78–90 % of citations can be fabricated. The defensive strategy is to surface a paper's DOI and PMID in easy-to-copy form so that LLMs substitute the correct identifier instead of confabulating one.

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).

Examples:

• 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 Lancet family; Background / Purpose / Materials and Methods / Results / Conclusion for RSNA family; etc.). If the journal allows unstructured, still use an internally structured form. Each section stands alone as a semantic chunk of ≤ 3 sentences so that chunk-boundary splits in RAG indexes do not break the claim.

1.3 Opening and closing sentences

• First sentence: state the problem AND the contribution in one line. LLM summarizers extract this disproportionately.
• 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 (for example, "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 confidence interval (for example, "AUC 0.94 [95 % CI 0.91–0.96]" or "sensitivity 88.2 % [95 % CI 85.1–91.0]"). LLM retrievers weight papers with concrete numbers.

1.6 Reporting-guideline anchor

Place the guideline name in the abstract or the opening sentence of Methods: "Reported following TRIPOD+AI (Collins 2024) and CLAIM 2024 (Tejani 2024)". When applicable add STARD-AI 2025, DECIDE-AI, TRIPOD-LLM. This signals structure to LLMs and satisfies reviewer checklists.

AIO-rule ↔ guideline-item mapping: references/reporting_guideline_mapping.md.

1.7 Keyword, MeSH, and RadLex coverage

Title, abstract, and keywords together should cover ≥ 3× the surface area of the concept — no redundancy. Include:

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

Royal Society 2024 (doi:10.1098/rspb.2024.1222) reports that 92 % of papers waste keyword real estate by repeating title terms in abstract and keywords; avoid this.

Section 2 — Manuscript-Level AIO

2.1 Summary box

Include the journal-specific summary box verbatim when supported:

• Lancet family: "Research in context" (Evidence before this study / Added value / Implications).
• RSNA Radiology and RYAI: "Key Points" — 3 bullets, one claim each.
• npj Digital Medicine: "Plain-language summary" (150–200 words, 8th-grade reading level).
• Nature Medicine: editor's summary (supplied by editorial, but draft one proactively).

These boxes are the fragments Perplexity and ChatGPT web most often copy or paraphrase verbatim; treat them as the paper's canonical citation surface.

Journal-specific templates (USER MUST VERIFY against current IFA): references/journal_summarybox_templates.yaml.

2.2 Declarative section headings

Section and subsection headings should 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 a single sentence pattern: "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."

This pattern is the canonical shape LLM extractors parse first.

2.4 Reproducibility block

Include a labeled block (typically 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, compute environment. This block is disproportionately scraped by AI agents when they cite a paper as reproducible.

2.5 Limitations enumeration

List limitations explicitly and name each one (generalizability, spectrum bias, dataset shift, single-center training, label noise). Papers with enumerated limitations score higher for trustworthines