MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting

• URL: http://arxiv.org/abs/2412.02971v1
• Date: Wed, 04 Dec 2024 02:32:53 GMT
• Title: MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting
• Authors: Arnold Caleb Asiimwe, Dídac Surís, Pranav Rajpurkar, Carl Vondrick,
• Abstract summary: In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. We propose a two-stage framework capable of pinpointing these errors and then making corrections, simulating an textitautocorrection process.
• Score: 31.710972402763527
• License:
• Abstract: In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. Using the MIMIC-CXR dataset, we first intentionally introduce a diverse range of errors into reports. Subsequently, we propose a two-stage framework capable of pinpointing these errors and then making corrections, simulating an \textit{autocorrection} process. This method aims to address the shortcomings of existing automated medical reporting systems, like factual errors and incorrect conclusions, enhancing report reliability in vital healthcare applications. Importantly, our approach could serve as a guardrail, ensuring the accuracy and trustworthiness of automated report generation. Experiments on established datasets and state of the art report generation models validate this method's potential in correcting medical reporting errors.

Related papers

• Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation [20.173287130474797]
  generative medical Vision Large Language Models (VLLMs) are prone to hallucinations and can produce inaccurate diagnostic information. We introduce a novel Semantic Consistency-Based Uncertainty Quantification framework that provides both report-level and sentence-level uncertainties. By abstaining from high-uncertainty reports, our approach improves factuality scores by $10$%, achieved by rejecting $20$% of reports.
  arXiv  Detail & Related papers  (2024-12-05T20:43:39Z)
• Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports [0.0]
  We propose a novel model for explainable fact-checking that identifies errors in findings and their locations indicated through the reports. We evaluate the resulting fact-checking model and its utility in correcting reports generated by several SOTA automated reporting tools.
  arXiv  Detail & Related papers  (2024-12-03T05:21:42Z)
• Resource-Efficient Medical Report Generation using Large Language Models [3.2627279988912194]
  Medical report generation is the task of automatically writing radiology reports for chest X-ray images. We propose a new framework leveraging vision-enabled Large Language Models (LLM) for the task of medical report generation.
  arXiv  Detail & Related papers  (2024-10-21T05:08:18Z)
• ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports [1.9106067578277455]
  We introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports. We developed error categories that capture common mistakes in both human and AI-generated reports. Our approach uses a novel sampling scheme to inject diverse errors while maintaining clinical plausibility.
  arXiv  Detail & Related papers  (2024-09-17T01:42:39Z)
• WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction [5.7931394318054155]
  We present our approach that achieved top performance in all three subtasks. For the MS dataset, which contains subtle errors, we developed a retrieval-based system. For the UW dataset, reflecting more realistic clinical notes, we created a pipeline of modules to detect, localize, and correct errors.
  arXiv  Detail & Related papers  (2024-04-22T19:31:45Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study [60.56194508762205]
  We reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. We present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models.
  arXiv  Detail & Related papers  (2023-04-21T11:54:44Z)
• Factual Error Correction for Abstractive Summaries Using Entity Retrieval [57.01193722520597]
  We propose an efficient factual error correction system RFEC based on entities retrieval post-editing process. RFEC retrieves the evidence sentences from the original document by comparing the sentences with the target summary. Next, RFEC detects the entity-level errors in the summaries by considering the evidence sentences and substitutes the wrong entities with the accurate entities from the evidence sentences.
  arXiv  Detail & Related papers  (2022-04-18T11:35:02Z)
• Variational Topic Inference for Chest X-Ray Report Generation [102.04931207504173]
  Report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successfully caption natural images. We propose variational topic inference for automatic report generation.
  arXiv  Detail & Related papers  (2021-07-15T13:34:38Z)
• Chest X-ray Report Generation through Fine-Grained Label Learning [46.352966049776875]
  We present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings.
  arXiv  Detail & Related papers  (2020-07-27T19:50:56Z)
• CLARA: Clinical Report Auto-completion [56.206459591367405]
  CLinicit Al it Report it Auto-completion (CLARA) is an interactive method that generates reports in a sentence by sentence fashion based on doctors' anchor words and partially completed sentences. In our experimental evaluation, CLARA achieved 0.393 CIDEr and 0.248 BLEU-4 on X-ray reports and 0.482 CIDEr and 0.491 BLEU-4 for EEG reports for sentence-level generation.
  arXiv  Detail & Related papers  (2020-02-26T18:45:00Z)

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.