IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agents

arXiv:2404.15488v1 Announce Type: new
Abstract: In natural language processing applied to the clinical domain, utilizing large language models has emerged as a promising avenue for error detection and correction on clinical notes, a knowledge-intensive task for which annotated data is scarce. This paper presents MedReAct'N'MedReFlex, which leverages a suite of four LLM-based medical agents. The MedReAct agent initiates the process by observing, analyzing, and taking action, generating trajectories to guide the search to target a potential error in the clinical …

abstract agents annotated data arxiv clinical cs.ai cs.cl cs.ma data detection domain error knowledge language language models language processing large language large language models medical natural natural language natural language processing notes processing type