A Review of Published Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging

• URL: http://arxiv.org/abs/2206.11502v1
• Date: Thu, 23 Jun 2022 06:57:33 GMT
• Title: A Review of Published Machine Learning Natural Language Processing Applications for Protocolling Radiology Imaging
• Authors: Nihal Raju (5), Michael Woodburn (1 and 5), Stefan Kachel (2 and 3), Jack O'Shaughnessy (5), Laurence Sorace (5), Natalie Yang (2), Ruth P Lim (2 and 4) ((1) Harvard University, Extension School, Cambridge, MA, USA, (2) Department of Radiology, The University of Melbourne, Parkville, (3) Department of Radiology, Columbia University in the City of New York, (4) Department of Surgery, Austin, The University of Melbourne, (5) Austin Hospital, Austin Health, Melbourne, Australia)
• Abstract summary: Machine learning (ML) is a subfield of Artificial intelligence (AI) and its applications in radiology are growing at an ever-accelerating rate. Natural language processing (NLP), which can be combined with ML for text interpretation tasks, also has many potential applications in radiology. One such application is automation of radiology protocolling, which involves interpreting a clinical radiology referral and selecting the appropriate imaging technique.
• Score: 0.02408121010538496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) is a subfield of Artificial intelligence (AI), and its applications in radiology are growing at an ever-accelerating rate. The most studied ML application is the automated interpretation of images. However, natural language processing (NLP), which can be combined with ML for text interpretation tasks, also has many potential applications in radiology. One such application is automation of radiology protocolling, which involves interpreting a clinical radiology referral and selecting the appropriate imaging technique. It is an essential task which ensures that the correct imaging is performed. However, the time that a radiologist must dedicate to protocolling could otherwise be spent reporting, communicating with referrers, or teaching. To date, there have been few publications in which ML models were developed that use clinical text to automate protocol selection. This article reviews the existing literature in this field. A systematic assessment of the published models is performed with reference to best practices suggested by machine learning convention. Progress towards implementing automated protocolling in a clinical setting is discussed.

Related papers

• Acoustic to Articulatory Inversion of Speech; Data Driven Approaches, Challenges, Applications, and Future Scope [0.0]
  This review is focused on the data-driven approaches applied in different applications of Acoustic-to-Articulatory Inversion (AAI) of speech.
  arXiv  Detail & Related papers  (2025-04-17T19:38:50Z)
• Best Practices for Large Language Models in Radiology [4.972411560978282]
  Nuanced application of language is key for various activities. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology.
  arXiv  Detail & Related papers  (2024-12-02T07:54:55Z)
• A Comparative Study on Automatic Coding of Medical Letters with Explainability [7.834930446531957]
  This study aims to explore the implementation of Natural Language Processing (NLP) and machine learning (ML) techniques to automate the coding of medical letters. We used the publicly available MIMIC-III database and the HAN/HLAN network models for ICD code prediction purposes. In our experiments, the models provided useful information for 97.98% of codes.
  arXiv  Detail & Related papers  (2024-07-18T16:12:47Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance [53.20640629352422]
  Conversational AI tools can generate and discuss clinically correct radiology reports for a given medical image. RaDialog is the first thoroughly evaluated and publicly available large vision-language model for radiology report generation and interactive dialog. Our method achieves state-of-the-art clinical correctness in report generation and shows impressive abilities in interactive tasks such as correcting reports and answering questions.
  arXiv  Detail & Related papers  (2023-11-30T16:28:40Z)
• 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)
• Can Prompt Learning Benefit Radiology Report Generation? [24.03963806122309]
  We propose PromptRRG, a method that utilizes prompt learning to activate a pretrained model and incorporate prior knowledge. This is the first work to systematically examine the effectiveness of prompt learning for radiology report generation.
  arXiv  Detail & Related papers  (2023-08-30T18:58:32Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• 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)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Future Artificial Intelligence tools and perspectives in medicine [1.7532045941271799]
  Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. This review explores the progress of AI-based radiomic tools for clinical applications with a brief description of necessary technical steps.
  arXiv  Detail & Related papers  (2022-06-04T11:27:43Z)
• Generating Radiology Reports via Memory-driven Transformer [38.30011851429407]
  We propose to generate radiology reports with memory-driven Transformer. Experimental results on two prevailing radiology report datasets, IU X-Ray and MIMIC-CXR.
  arXiv  Detail & Related papers  (2020-10-30T04:08:03Z)
• A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images [50.222197963803644]
  Niffler is an integrated framework that enables the execution of machine learning pipelines at research clusters. Niffler uses the Digital Imaging and Communications in Medicine (DICOM) protocol to fetch and store imaging data. We present its architecture and three of its use cases: an inferior vena cava filter detection from the images in real-time, identification of scanner utilization, and scanner clock calibration.
  arXiv  Detail & Related papers  (2020-04-16T21:06:49Z)

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.