# MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports Source: [MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:02:45.345Z Authors: Sunggu Kyung, Hyungbin Park, Jinyoung Seo, Jimin Sung, Jihyun Kim, Dongyeong Kim, Wooyoung Jo, Yoojin Nam, Sangah Park, Taehee Kwon, Sang Min Lee, Namkug Kim Published: 2025-06-24T00:51:03.000Z ## Abstract Computed Tomography (CT) plays a crucial role in clinical diagnosis, but the growing demand for CT examinations has raised concerns about diagnostic errors. While Multimodal Large Language Models (MLLMs) demonstrate promising comprehension of medical knowledge, their tendency to produce inaccurate information highlights the need for rigorous validation. However, existing medical visual question answering (VQA) benchmarks primarily focus on simple visual recognition tasks, lacking clinical relevance and failing to assess expert-level knowledge. We introduce MedErr-CT, a novel benchmark for evaluating medical MLLMs' ability to identify and correct errors in CT reports through a VQA framework. The benchmark includes six error categories - four vision-centric errors (Omission, Insertion, Direction, Size) and two lexical error types (Unit, Typo) - and is organized into three task levels: classification, detection, and correction. Using this benchmark, we quantitatively assess the performance of state-of-the-art 3D medical MLLMs, revealing substantial variation in their capabilities across different error types. Our benchmark contributes to the development of more reliable and clinically applicable MLLMs, ultimately helping reduce diagnostic errors and improve accuracy in clinical practice. The code and datasets are available at https://github.com/babbu3682/MedErr-CT. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()