mlsanityData to 14 Oct 2026

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

arXiv 2404.15488 ↗First posted 23 Apr 2024cs.CL cs.AI cs.MAcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

25 of 43 references.

References · 25

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13 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignmentcited by 20
  2. 2025Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applicationscited by 13
  3. 2025Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applicationscited by 7
  4. 2026MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotationscited by 2
  5. 2026MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correctioncited by 0
  6. 2026Toward Better Assessment of LLMs' Performance in Clinical Error Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts
  2. 2024MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes
  3. 2024PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM Ensembles
  4. 2026BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection
  5. 2025A Systematic Analysis of Large Language Models with RAG-enabled Dynamic Prompting for Medical Error Detection and Correction
  6. 2025Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
  7. 2025MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
  8. 2026AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization