ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment

• URL: http://arxiv.org/abs/2510.00280v1
• Date: Tue, 30 Sep 2025 21:00:47 GMT
• Title: ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment
• Authors: Ruochen Li, Jun Li, Bailiang Jian, Kun Yuan, Youxiang Zhu,
• Abstract summary: We propose a clinically grounded Meta-Evaluation framework.<n>We define clinically grounded criteria spanning clinical alignment and key metric capabilities.<n>Our framework offers guidance for building more clinically reliable evaluation methods.
• Score: 10.958326795130112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians' trust. This gap reveals fundamental flaws in how current metrics assess the quality of generated reports. We rethink the design and evaluation of these metrics and propose a clinically grounded Meta-Evaluation framework. We define clinically grounded criteria spanning clinical alignment and key metric capabilities, including discrimination, robustness, and monotonicity. Using a fine-grained dataset of ground truth and rewritten report pairs annotated with error types, clinical significance labels, and explanations, we systematically evaluate existing metrics and reveal their limitations in interpreting clinical semantics, such as failing to distinguish clinically significant errors, over-penalizing harmless variations, and lacking consistency across error severity levels. Our framework offers guidance for building more clinically reliable evaluation methods.

Related papers

• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning [58.01333341218153]
  We propose ClinDEF, a dynamic framework for assessing clinical reasoning in LLMs through simulated diagnostic dialogues.<n>Our method generates patient cases and facilitates multi-turn interactions between an LLM-based doctor and an automated patient agent.<n>Experiments show that ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-12-29T12:58:58Z)
• Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation [32.410641778559544]
  ICARE (Interpretable and Clinically-grounded Agent-based Report Evaluation) is an interpretable evaluation framework.<n>Two agents, each with either the ground-truth or generated report, generate clinically meaningful questions and quiz each other.<n>By linking scores to question-answer pairs, ICARE enables transparent, and interpretable assessment.
  arXiv  Detail & Related papers  (2025-08-04T18:28:03Z)
• CLEAR: A Clinically-Grounded Tabular Framework for Radiology Report Evaluation [14.580143601537957]
  We introduce a Clinically-grounded framework with Expert-curated labels and Attribute-level comparison for Radiology report evaluation (CLEAR)<n>CLEAR examines whether a report can accurately identify the presence or absence of medical conditions.<n>To measure the clinical alignment of CLEAR, we collaborate with five board-certified radiologists to develop CLEAR-Bench, a dataset of 100 chest X-ray reports from MIMIC-CXR.
  arXiv  Detail & Related papers  (2025-05-22T07:32:12Z)
• Metrics that matter: Evaluating image quality metrics for medical image generation [48.85783422900129]
  This study comprehensively assesses commonly used no-reference image quality metrics using brain MRI data.<n>We evaluate metric sensitivity to a range of challenges, including noise, distribution shifts, and, critically, morphological alterations designed to mimic clinically relevant inaccuracies.
  arXiv  Detail & Related papers  (2025-05-12T01:57:25Z)
• Med-CoDE: Medical Critique based Disagreement Evaluation Framework [72.42301910238861]
  The reliability and accuracy of large language models (LLMs) in medical contexts remain critical concerns.<n>Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance.<n>We propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges.
  arXiv  Detail & Related papers  (2025-04-21T16:51:11Z)
• GEMA-Score: Granular Explainable Multi-Agent Scoring Framework for Radiology Report Evaluation [7.838068874909676]
  Granular Explainable Multi-Agent Score (GEMA-Score) conducts both objective and subjective evaluation through a large language model-based multi-agent workflow.<n>GEMA-Score achieves the highest correlation with human expert evaluations on a public dataset.
  arXiv  Detail & Related papers  (2025-03-07T11:42:22Z)
• Pitfalls of topology-aware image segmentation [81.19923502845441]
  We identify critical pitfalls in model evaluation that include inadequate connectivity choices, overlooked topological artifacts, and inappropriate use of evaluation metrics.<n>We propose a set of actionable recommendations to establish fair and robust evaluation standards for topology-aware medical image segmentation methods.
  arXiv  Detail & Related papers  (2024-12-19T08:11:42Z)
• Image-aware Evaluation of Generated Medical Reports [11.190146577567548]
  The paper proposes a novel evaluation metric for automatic medical report generation from X-ray images, VLScore. The key idea of our metric is to measure the similarity between radiology reports while considering the corresponding image. We demonstrate the benefit of our metric through evaluation on a dataset where radiologists marked errors in pairs of reports, showing notable alignment with radiologists' judgments.
  arXiv  Detail & Related papers  (2024-10-22T18:50:20Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• GO FIGURE: A Meta Evaluation of Factuality in Summarization [131.1087461486504]
  We introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. Our benchmark analysis on ten factuality metrics reveals that our framework provides a robust and efficient evaluation. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.
  arXiv  Detail & Related papers  (2020-10-24T08:30:20Z)

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.