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

arXiv: https://arxiv.org/abs/2404.17999 · HTML: https://mlsanity.com/paper/2404.17999/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 27 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)

## Built on by (most cited first, 3 of 3)

- [MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations](https://mlsanity.com/paper/2602.05692/) (2026)
- [Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models](https://mlsanity.com/paper/2602.22483/) (2026)
- [MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction](https://mlsanity.com/paper/2606.25651/) (2026)

## Abstract (arXiv)

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.