Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary

• URL: http://arxiv.org/abs/2406.14500v1
• Date: Thu, 20 Jun 2024 17:01:55 GMT
• Title: Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary
• Authors: Xingmeng Zhao, Tongnian Wang, Anthony Rios,
• Abstract summary: Radiology report summarization (RRS) is crucial for patient care, requiring concise "Impressions" from detailed "Findings" This paper introduces a novel prompting strategy to enhance RRS by first generating a layperson summary. Our results demonstrate improvements in summarization accuracy and accessibility, particularly in out-of-domain tests.
• Score: 8.003346409136348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radiology report summarization (RRS) is crucial for patient care, requiring concise "Impressions" from detailed "Findings." This paper introduces a novel prompting strategy to enhance RRS by first generating a layperson summary. This approach normalizes key observations and simplifies complex information using non-expert communication techniques inspired by doctor-patient interactions. Combined with few-shot in-context learning, this method improves the model's ability to link general terms to specific findings. We evaluate this approach on the MIMIC-CXR, CheXpert, and MIMIC-III datasets, benchmarking it against 7B/8B parameter state-of-the-art open-source large language models (LLMs) like Meta-Llama-3-8B-Instruct. Our results demonstrate improvements in summarization accuracy and accessibility, particularly in out-of-domain tests, with improvements as high as 5% for some metrics.

Related papers

• Improving Radiology Report Conciseness and Structure via Local Large Language Models [0.0]
  We aim to enhance radiology reporting by improving the conciseness and structured organization of findings. This structured approach allows physicians to locate relevant information quickly, increasing the report's utility. We utilize Large Language Models (LLMs) such as Mixtral, Mistral, and Llama to generate concise, well-structured reports.
  arXiv  Detail & Related papers  (2024-11-06T19:00:57Z)
• Enhanced Electronic Health Records Text Summarization Using Large Language Models [0.0]
  This project builds on prior work by creating a system that generates clinician-preferred, focused summaries. The proposed system leverages the Flan-T5 model to generate tailored EHR summaries based on clinician-specified topics.
  arXiv  Detail & Related papers  (2024-10-12T19:36:41Z)
• RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization [1.8450534779202723]
  This study proposes RadBARTsum, a domain-specific and facilitated adaptation of the BART model for abstractive radiology report summarization. The approach involves two main steps: 1) re-training the BART model on a large corpus of radiology reports using a novel entity masking strategy to improve biomedical domain knowledge learning, and 2) fine-tuning the model for the summarization task using the Findings and Background sections to predict the Impression section.
  arXiv  Detail & Related papers  (2024-06-05T08:43:11Z)
• EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling [22.94521527609479]
  EMERGE is a Retrieval-Augmented Generation driven framework aimed at enhancing multimodal EHR predictive modeling. Our approach extracts entities from both time-series data and clinical notes by prompting Large Language Models. The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses.
  arXiv  Detail & Related papers  (2024-05-27T10:53:15Z)
• Information-Theoretic Distillation for Reference-less Summarization [67.51150817011617]
  We present a novel framework to distill a powerful summarizer based on the information-theoretic objective for summarization. We start off from Pythia-2.8B as the teacher model, which is not yet capable of summarization. We arrive at a compact but powerful summarizer with only 568M parameters that performs competitively against ChatGPT.
  arXiv  Detail & Related papers  (2024-03-20T17:42:08Z)
• ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs [60.81649785463651]
  We introduce ExaRanker-Open, where we adapt and explore the use of open-source language models to generate explanations. Our findings reveal that incorporating explanations consistently enhances neural rankers, with benefits escalating as the LLM size increases.
  arXiv  Detail & Related papers  (2024-02-09T11:23:14Z)
• 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)
• 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)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

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.