Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture

Researchers present MedSci Skills, an open-source toolkit that pairs LLM-assisted clinical manuscript generation with deterministic verification gates to detect fabricated citations, numerical errors, and missing reporting guidelines. The system demonstrates 100% detection of seeded defects versus 41% for generic LLM reviewers, providing an auditable trail for biomedical publishing.

This research addresses a critical vulnerability in AI-assisted academic publishing: language models generate fluent text that can obscure fabrication, numerical drift, and guideline non-compliance. The MedSci Skills architecture tackles this by decomposing manuscript preparation into discrete skills, then gating each transition with halt-on-failure verification. The innovation lies in the integrity-gate taxonomy—using deterministic, re-executable checks (computational hash validation, pattern matching) where possible, and prose-level LLM review only when interpretation is unavoidable. This split dramatically reduces false negatives while maintaining auditability.

The background here reflects growing awareness that LLM fluency masks unreliability. Prior systems generate and self-critique using the same model, inheriting and amplifying its blind spots. Clinical publishing faces unique stakes: fabricated data or missing methodology can affect patient care. The toolkit's evaluation across STARD, PRISMA, and STROBE datasets—three major reporting standards—shows perfect manifest verification and real-defect detection. Critically, on 27 injected defects, deterministic gates caught all 27 with zero false positives, while a single-prompt LLM reviewer missed 16, particularly in code generation and bibliographic consistency.

For the biomedical and AI communities, this establishes that hybrid human-AI workflows require asymmetric trust: generation can remain probabilistic if verification is deterministic. The open-source MIT license and reproducible pipelines lower adoption barriers. Publishers and institutional review boards may adopt similar gating architectures. The work does not claim human-competitive quality but instead provides evidence trails auditors need, shifting the responsibility model from model capability to process transparency.

• →Deterministic verification gates detect 100% of seeded defects versus 41% for generic LLM reviewers in clinical manuscripts
• →The integrity-gate taxonomy prioritizes computational checks over prose-level LLM review to reduce hallucination-inherited blind spots
• →MedSci Skills toolkit coordinates 43 skills with 21 deterministic detectors across STARD, PRISMA, and STROBE reporting standards
• →Hybrid workflows should use probabilistic generation paired with deterministic verification rather than self-critique loops
• →Open-source architecture and reproducible public datasets enable institutional adoption for biomedical publishing workflows