Vollständiger Abstract
Worum geht es in dieser Arbeit?
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.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Quan Zhang, Yuanyuan Fu
- Quelle
- npj Digital Medicine
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2398-6352
- Zitationen
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Zitierfähiger Nachweis
Quan Zhang, Yuanyuan Fu (2026). Ensuring trustworthy AI assisted guideline development for clinical practice. npj Digital Medicine. https://doi.org/10.1038/s41746-026-03098-z
Kontext
Themen, Förderung und Nutzung
Lizenzhinweise: Lizenz 1