Large language models in healthcare quality management: a European perspective on process automation and compliance

Large Language Models (LLMs) are transforming back-office quality management processes in European healthcare systems through automation of compliance monitoring, quality assurance, and process optimization without direct patient interaction. This narrative review synthesizes evidence from recent systematic reviews and implementation studies (2023-2025) examining LLM deployment within the European regulatory framework encompassing the Medical Device Regulation (MDR), General Data Protection Regulation (GDPR), and the EU Artificial Intelligence Act (Regulation EU 2024/1689). Current research demonstrates meaningful efficiency gains: individual studies of AI-assisted documentation tools report improvements ranging from modest increases in documentation speed to reductions in processing time approaching 50%, while broader policy analyses estimate administrative workload reductions of up to 30% through digital health and AI solutions. Clinical trial applications show particular maturity, with LLM-generated informed consent forms demonstrating improved readability (76% vs. 67%) without compromising accuracy. However, critical gaps persist between research achievements and practical deployment. Analysis of 519 evaluation studies reveals that only 5% utilized real patient care data, while 95% focused exclusively on accuracy metrics to the neglect of fairness (16%), deployment readiness (5%), and calibration (1%). No LLM-based quality management system has yet received regulatory clearance, and implementation science frameworks remain underdeveloped. We propose a risk-stratified implementation framework emphasizing process-oriented applications-standard operating procedure automation, audit documentation, deviation management, and compliance monitoring-that avoid medical device classification while capturing substantial operational benefits. Advanced methodological approaches including retrieval-augmented generation (RAG) architectures, digital twin integration, and natural language processing-based pattern recognition offer pathways toward comprehensive quality intelligence platforms. The convergence of LLMs with emerging technologies such as knowledge graphs, digital twin architectures and multimodal analysis creates opportunities for predictive quality management that anticipates rather than merely documents quality-relevant events. Evidence supports deployment in administrative quality processes, with particular potential for applications that redirect human expertise from documentation toward quality improvement activities, though current evidence derives predominantly from non-European healthcare contexts and simulated or limited-scope settings. Success requires adapted validation methodologies addressing LLM non-determinism, robust governance structures, and comprehensive change management that maintains the high standards European healthcare systems demand.