LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation

• URL: http://arxiv.org/abs/2404.00998v1
• Date: Mon, 1 Apr 2024 09:02:12 GMT
• Title: LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation
• Authors: Zilong Wang, Xufang Luo, Xinyang Jiang, Dongsheng Li, Lili Qiu,
• Abstract summary: evaluating generated radiology reports is crucial for the development of radiology AI. This study proposes a novel evaluation framework using large language models (LLMs) to compare radiology reports for assessment.
• Score: 37.20505633019773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating generated radiology reports is crucial for the development of radiology AI, but existing metrics fail to reflect the task's clinical requirements. This study proposes a novel evaluation framework using large language models (LLMs) to compare radiology reports for assessment. We compare the performance of various LLMs and demonstrate that, when using GPT-4, our proposed metric achieves evaluation consistency close to that of radiologists. Furthermore, to reduce costs and improve accessibility, making this method practical, we construct a dataset using LLM evaluation results and perform knowledge distillation to train a smaller model. The distilled model achieves evaluation capabilities comparable to GPT-4. Our framework and distilled model offer an accessible and efficient evaluation method for radiology report generation, facilitating the development of more clinically relevant models. The model will be further open-sourced and accessible.

Related papers

• Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs [18.025481751074214]
  We introduce a system, named ReXKG, which extracts structured information from processed reports to construct a radiology knowledge graph. We conduct an in-depth comparative analysis of AI-generated and human-written radiology reports, assessing the performance of both specialist and generalist models.
  arXiv  Detail & Related papers  (2024-08-26T16:28:56Z)
• RaTEScore: A Metric for Radiology Report Generation [59.37561810438641]
  This paper introduces a novel, entity-aware metric, as Radiological Report (Text) Evaluation (RaTEScore) RaTEScore emphasizes crucial medical entities such as diagnostic outcomes and anatomical details, and is robust against complex medical synonyms and sensitive to negation expressions. Our evaluations demonstrate that RaTEScore aligns more closely with human preference than existing metrics, validated both on established public benchmarks and our newly proposed RaTE-Eval benchmark.
  arXiv  Detail & Related papers  (2024-06-24T17:49:28Z)
• MRScore: Evaluating Radiology Report Generation with LLM-based Reward System [39.54237580336297]
  This paper introduces MRScore, an automatic evaluation metric tailored for radiology report generation by leveraging Large Language Models (LLMs) To address this challenge, we collaborated with radiologists to develop a framework that guides LLMs for radiology report evaluation, ensuring alignment with human analysis. Our experiments demonstrate MRScore's higher correlation with human judgments and superior performance in model selection compared to traditional metrics.
  arXiv  Detail & Related papers  (2024-04-27T04:42:45Z)
• Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning [16.849933628738277]
  Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. This paper introduces a novel RRG method, textbfLM-RRG, that integrates large models (LMs) with clinical quality reinforcement learning. Experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.
  arXiv  Detail & Related papers  (2024-03-11T13:47:11Z)
• Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports [22.599250713630333]
  Our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs) Our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI generated reports. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a 0.48 score, outperforming the METEOR metric by 0.19.
  arXiv  Detail & Related papers  (2024-01-29T21:24:43Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• Radiology-Llama2: Best-in-Class Large Language Model for Radiology [71.27700230067168]
  This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-08-29T17:44:28Z)
• An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT [80.33783969507458]
  The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Recent studies have achieved promising results in automatic impression generation using large-scale medical text data. These models often require substantial amounts of medical text data and have poor generalization performance.
  arXiv  Detail & Related papers  (2023-04-17T17:13:42Z)

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.