MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch

TL;DR

This paper introduces a novel, domain-specific approach for correcting single-word errors in clinical notes, emphasizing the importance of human expertise and context-aware methods to improve AI accuracy in healthcare documentation.

Contribution

The paper presents a supervised learning framework combining extractive and abstractive QA methods, integrating domain knowledge to enhance clinical text error correction.

Findings

Improved accuracy in clinical note error correction

Effective use of domain-specific features

Highlighting the importance of human-AI collaboration in healthcare

Abstract

Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare.