Exploring Multimodal Large Language Models for Radiology Report Error-checking

arXiv:2312.13103v2 Announce Type: replace-cross
Abstract: This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-rays and CT scans). A subset of original reports was modified to contain synthetic errors by introducing three types of mistakes: "insert", "remove", and "substitute". The evaluation contained two difficulty levels: SIMPLE for binary error-checking and COMPLEX for identifying …

abstract applications arxiv assistant check clinical cs.cl cs.cv dataset datasets error errors evaluation language language models large language large language models llms multimodal paper radiology report reports scans type world