ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment (2025.emnlp-main)
Copied to clipboard
| Challenge: | Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians’ trust. |
| Approach: | They propose a meta-evaluation framework that uses criteria spanning discrimination, robustness, and monotonicity to evaluate existing metrics. |
| Outcome: | The proposed framework offers guidance for building more clinically reliable evaluation methods. |
Similar Papers
CLEAR: A Clinically Grounded Tabular Framework for Radiology Report Evaluation (2025.findings-emnlp)
Copied to clipboard
Yuyang Jiang, Chacha Chen, Shengyuan Wang, Feng Li, Zecong Tang, Benjamin M. Mervak, Lydia Chelala, Christopher M Straus, Reve Chahine, Samuel G. Armato Iii, Chenhao Tan
| Challenge: | Existing metrics lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports. |
| Approach: | They propose a tabular framework with E**xpert-curated labels and an attribute-level comparison for radiology report evaluation (**CLEAR) |
| Outcome: | The proposed framework can extract clinical attributes and provide automated metrics that are strongly aligned with clinical judgment. |
GREEN: Generative Radiology Report Evaluation and Error Notation (2024.findings-emnlp)
Copied to clipboard
Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Md, Michael Moseley, Curtis Langlotz, Akshay Chaudhari, Jean-Benoit Delbrouck
| Challenge: | Existing automated evaluation metrics fail to consider factual correctness or are limited in their interpretability. |
| Approach: | They propose a radiology report evaluation metric that leverages natural language understanding of language models to identify and explain clinically significant errors. |
| Outcome: | The proposed method demonstrates higher correlation with expert error counts and higher alignment with expert preferences when compared to previous methods. |
TRUE: Re-evaluating Factual Consistency Evaluation (2022.naacl-main)
Copied to clipboard
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, Yossi Matias
| Challenge: | Grounded text generation systems often generate factual inconsistencies, hindering their real-world applicability. |
| Approach: | They propose a method to assess factual consistency metrics on standardized texts . they recommend NLI and question generation-and-answering-based methods as starting points . |
| Outcome: | The proposed method is more actionable and interpretable than previous methods. |
From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing automated metrics fail to align with real-world physician preferences. |
| Approach: | They propose a pipeline that distills real user feedback into structured checklists for note evaluation that are interpretable, grounded in human feedback, and enforceable by LLM-based evaluators. |
| Outcome: | The proposed checklist outperforms baseline evaluations in coverage, diversity, and predictive power for human ratings. |
X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation (2026.findings-acl)
Copied to clipboard
Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
| Challenge: | Technical language and templated nature of professional reports hinder patient comprehension and allow models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. |
| Approach: | They propose a layman's RRG framework that leverages layperson-friendly language to enhance patient accessibility and promote robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. |
| Outcome: | The proposed framework improves model performance with more layman-style data, compared to templated professional language and inflated lexical scores. |
DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)
Copied to clipboard
Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
Learning to Generate Clinically Coherent Chest X-Ray Reports (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing abstractive methods for radiology report generation produce fluent, but clinically incorrect reports. |
| Approach: | They propose a radiology report generation model that uses the transformer architecture to extract clinical information from generated reports and fine-tune the model to produce more clinically coherent reports. |
| Outcome: | The proposed model produces superior reports as measured by standard language generation and clinical coherence metrics compared to competitive baselines. |
RaTEScore: A Metric for Radiology Report Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing metrics to evaluate the quality of medical reports are limited due to the complexity of clinical free-form texts. |
| Approach: | They propose a new metric to assess the quality of medical reports generated by AI models. |
| Outcome: | The proposed metric is based on a medical NER dataset and trained on NER models . it aligns more closely with human preference than existing metrics, the authors show . |
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022.findings-emnlp)
Copied to clipboard
Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
| Approach: | They propose a method to improve the factual completeness and correctness of generated radiology reports by using a dataset containing annotated chest X-ray images. |
| Outcome: | The proposed method significantly improves factual completeness and correctness of generated radiology reports on two open radiology report datasets. |
An Investigation of Evaluation Methods in Automatic Medical Note Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies show that doctors can save significant amounts of time when using automatic note generation. |
| Approach: | They propose task-specific metrics for automatic note generation from medical conversation summarization and generation, including knowledge-graph embedding-based metrics, customized model-based measures with domain-specific weights, and ensemble metrics. |
| Outcome: | The proposed evaluation metrics are compared to existing models and can have different behaviors on different types of clinical notes datasets. |