Academic AIO applies Generative Engine Optimization (GEO) principles and clinical reporting guidelines (TRIPOD+AI, CLAIM 2024, STARD-AI) to medical AI manuscripts, abstracts, and open-source release artifacts. It audits and refines titles, structured summaries (such as Lancet Research in Context and RSNA Key Points), quantified claim density, reproducibility blocks, and CITATION.cff/Zenodo metadata to ensure research is surfaced faithfully by AI search engines like Perplexity, Elicit, Consensus, and ChatGPT without confabulation or hallucinated citations.
主要功能
01Ensures alignment with reporting guidelines (TRIPOD+AI, CLAIM 2024, STARD-AI, DECIDE-AI)
02Optimizes title structure, MeSH/RadLex keyword coverage, and semantic chunking for RAG
03Structures machine-readable release artifacts including CITATION.cff, Zenodo DOIs, and repo metadata
04Validates and formats journal-specific summary boxes (Lancet, RSNA, npj Digital Medicine)
05269 GitHub stars
06Generates an actionable PASS/PARTIAL/FAIL discoverability and AIO audit checklist
使用场景
01Refining titles, abstracts, and summary boxes prior to submitting to high-impact digital health journals
02Auditing medical AI preprints, GitHub READMEs, and Hugging Face model cards for AI search engine visibility
03Protecting research from LLM citation hallucination by formatting explicit machine-readable metadata and DOIs