  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 notes.
Subsequently, the MedEval agent employs five evaluators to assess the targeted
error and the proposed correction. In cases where MedReAct's actions prove
insufficient, the MedReFlex agent intervenes, engaging in reflective analysis
and proposing alternative strategies. Finally, the MedFinalParser agent formats
the final output, preserving the original style while ensuring the integrity of
the error correction process. One core component of our method is our RAG
pipeline based on our ClinicalCorp corpora. Among other well-known sources
containing clinical guidelines and information, we preprocess and release the
open-source MedWiki dataset for clinical RAG application. Our results
demonstrate the central role of our RAG approach with ClinicalCorp leveraged
through the MedReAct'N'MedReFlex framework. It achieved the ninth rank on the
MEDIQA-CORR 2024 final leaderboard.
