MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports

• URL: http://arxiv.org/abs/2506.19217v1
• Date: Tue, 24 Jun 2025 00:51:03 GMT
• Title: MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports
• 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,
• Abstract summary: We introduce MedErr-CT, a novel benchmark for evaluating medical MLLMs' ability to identify and correct errors in CT reports.<n>The benchmark includes six error categories - four vision-centric errors (Omission, Insertion, Direction, Size) and two lexical error types (Unit, Typo)
• Score: 4.769418278782809
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation [29.547871298286687]
  We introduce a knowledge-guided data augmentation framework that enhances the difficulty of clinical datasets by generating distractors.<n>Our approach involves multi-step, semantically informed walks on a medical knowledge graph to identify distractor paths-associations that are medically relevant but factually incorrect.<n>We apply the designed knowledge graph guided distractor generation (KGGDG) pipline, to six widely used medical QA benchmarks and show that it consistently reduces the accuracy of state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-05-31T15:51:09Z)
• Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering [8.185551155349241]
  Vision-Language Models (VLMs) have shown promise in various 2D visual tasks, yet their readiness for 3D clinical diagnosis remains unclear.<n>We present DeepTumorVQA, a diagnostic visual question answering benchmark targeting abdominal tumors in CT scans.<n>It comprises 9,262 CT volumes (3.7M slices) from 17 public datasets, with 395K expert-level questions spanning four categories: Recognition, Measurement, Visual Reasoning, and Medical Reasoning.
  arXiv  Detail & Related papers  (2025-05-25T00:50:15Z)
• MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports [49.00805568780791]
  We introduce MedCaseReasoning, the first open-access dataset for evaluating Large Language Models (LLMs) on their ability to align with clinician-authored diagnostic reasoning.<n>The dataset includes 14,489 diagnostic question-and-answer cases, each paired with detailed reasoning statements.<n>We evaluate state-of-the-art reasoning LLMs on MedCaseReasoning and find significant shortcomings in their diagnoses and reasoning.
  arXiv  Detail & Related papers  (2025-05-16T22:34:36Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Fact or Guesswork? Evaluating Large Language Model's Medical Knowledge with Structured One-Hop Judgment [108.55277188617035]
  Large language models (LLMs) have been widely adopted in various downstream task domains, but their ability to directly recall and apply factual medical knowledge remains under-explored.<n>Most existing medical QA benchmarks assess complex reasoning or multi-hop inference, making it difficult to isolate LLMs' inherent medical knowledge from their reasoning capabilities.<n>We introduce the Medical Knowledge Judgment, a dataset specifically designed to measure LLMs' one-hop factual medical knowledge.
  arXiv  Detail & Related papers  (2025-02-20T05:27:51Z)
• MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes [22.401540975926324]
  We introduce MEDEC, the first publicly available benchmark for medical error detection and correction in clinical notes.<n> MEDEC consists of 3,848 clinical texts, including 488 clinical notes from three US hospital systems.<n>We evaluate recent LLMs for the tasks of detecting and correcting medical errors requiring both medical knowledge and reasoning capabilities.
  arXiv  Detail & Related papers  (2024-12-26T15:54:10Z)
• MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement [1.6355783973385114]
  Multi-view perception knowledge-enhanced TansfoRmer (MvKeTR)<n>MVPA with view-aware attention is proposed to synthesize diagnostic information from multiple anatomical views effectively.<n>Cross-Modal Knowledge Enhancer (CMKE) is devised to retrieve the most similar reports based on the query volume.
  arXiv  Detail & Related papers  (2024-11-27T12:58:23Z)
• Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA [24.10436440624249]
  Large Multimodal Models (LMMs) have shown remarkable progress in medical Visual Question Answering (Med-VQA)<n>This study reveals that when subjected to simple probing evaluation, state-of-the-art models perform worse than random guessing on medical diagnosis questions.
  arXiv  Detail & Related papers  (2024-05-30T18:56:01Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.