MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction

A new framework called MedGuards proposes a multi-agent approach to detect and correct errors in medical text generated by large language models, aiming to improve patient safety without retraining the underlying models [1]. The system, detailed in a paper submitted to arXiv on June 24, 2026, treats error detection and correction as a multi-agent in-context learning task [1][2]. Specialized agents within MedGuards separately detect, localize, and correct errors, while a confidence-guided arbitration mechanism resolves disagreements using reasoning traces and confidence scores [1][2]. This design is intended to enhance interpretability, robustness, and adaptability [1][2]. The paper's authors also introduce a new evaluation metric called the Keyword-Prioritized Correction Score, or KPCS, which assesses whether critical keywords are generated correctly, providing a more comprehensive assessment than conventional metrics [1][2]. Experiments were conducted across four multilingual medical datasets consisting of clinical notes, and the authors report significant improvements across several metrics and models [1][2]. The code has been made publicly available on GitHub for reproducibility [1][2]. The research arrives as large language models, which are machine learning models with many parameters trained on vast amounts of text, see increasing deployment in healthcare settings [1][8]. The paper appears on arXiv, an open-access repository of electronic preprints that, as of late 2024, receives about 24,000 submissions per month and is not peer-reviewed [6]. The work is featured alongside experimental community tools under the arXivLabs framework, a program that allows collaborators to develop and share new features on the site, though new project proposals are temporarily paused while the platform's systems are modernized and moved to the cloud [3][4][5].