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

arXiv:2404.17999v1 Announce Type: cross
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 …

abstract artificial artificial intelligence arxiv challenges clinical cs.ai cs.cl cs.lg error free human information intelligence interpretation language language models large language large language models llms medical novel paper patient representation safety systems text type