Ensuring trustworthy AI assisted guideline development for clinical practice

Abstract

Li et al. introduce Quicker, an agentic large language model system that drafts clinical guideline recommendations through a GRADE-based workflow. Building on gaps in the authors’ own evaluation, we propose safeguards for trustworthy use: verifiable, design-aware evidence bundles with explicit scope; calibrated, uncertainty-aware deferral; targeted human validation and modular quality gates; and governance against contamination and over-reliance. Trustworthiness, however, is necessary but not sufficient for clinician trust.