Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

arXiv:2406.10185v1 Announce Type: new
Abstract: Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications, including medical visual question answering and imaging report generation. While these models inherit the robust capabilities of foundational Large Language Models (LLMs), they also inherit susceptibility to hallucinations-a significant concern in high-stakes medical contexts where the margin for error is minimal. However, currently, there are no dedicated methods or benchmarks for hallucination detection and evaluation in the medical field. To bridge this gap, we …

abstract applications arxiv capabilities cs.cv foundational hallucinations healthcare imaging integral language language models large language large language models llms medical question question answering report robust type vision visual while