Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture
arXiv:2606.09500v1 Announce Type: new Abstract: Objective. Large language models (LLMs) increasingly draft clinical research manuscripts, but their fluency can hide fabricated citations, numbers that drift from source tables, and unmet reporting-guideline items. Existing tools generate text without verifying it, and self-critique inherits the blind spots that produce confident fabrication. We describe an architecture that pairs generation with verification. Methods. The design rests on three principles: decompose the workflow into self-contained skills, gate every stage transition with halt-on
The proliferation of LLMs in professional domains, including sensitive areas like clinical research, necessitates robust verification mechanisms to combat inherent risks of hallucination and fact fabrication.
This development addresses critical trust and reliability issues in AI-generated content, particularly in fields where accuracy is paramount, thereby enabling wider and safer LLM adoption in specialized workflows.
The emphasis shifts from purely generative AI to integrated generation-and-verification architectures, introducing a critical layer of auditable integrity for LLM applications.
- · Biomedical researchers and institutions
- · AI verification tool developers
- · Clinical research organizations
- · Patients (indirectly, through more reliable research)
- · LLM developers without integrated verification
- · Publishers (if auditing unverified publications ex-post)
- · Relying solely on LLM self-critique
Increased confidence in LLM-assisted scientific writing and data analysis, particularly in fields with high stakes.
Development of specialized 'integrity gate' AI agents and platforms tailored for various industries, beyond just biomedical, requiring high accuracy.
Potential for new regulatory frameworks and industry standards mandating such verification layers for AI-generated critical content, impacting compliance costs and market entry.
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Read at arXiv cs.AI