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Related Concept Videos

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Advancements in Radiology Report Generation: A Comprehensive Analysis.

Dima Mamdouh1, Mariam Attia1, Mohamed Osama1

  • 1Center for Informatics Science, School of Information Technology and Computer Science (ITCS), Nile University, Giza 12588, Egypt.

Bioengineering (Basel, Switzerland)
|July 29, 2025
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Summary
This summary is machine-generated.

Artificial intelligence (AI) offers solutions for radiology report generation (RRG) challenges, using transformer models, vision-language models (VLMs), and Large Language Models (LLMs) to improve efficiency and accuracy in diagnostic reporting.

Keywords:
artificial intelligencecomputer visiongraphsmedical imagingnatural language processingradiology report generationtransformers

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Area of Science:

  • Medical Imaging and Radiology
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Increasing demand for radiological services and radiologist shortages create workload challenges.
  • Ensuring accuracy and timeliness of radiological reports is critical for clinical decision-making.
  • Artificial intelligence (AI) presents potential solutions for radiology report generation (RRG).

Purpose of the Study:

  • To provide a comprehensive overview of AI-driven RRG developments from 2021 to 2025.
  • To focus on emerging transformer-based and vision-language models (VLMs) in RRG.
  • To analyze datasets, evaluation metrics, and leading model performance in RRG.

Main Methods:

  • Review of transformer models, VLMs, and Large Language Models (LLMs) for RRG.
  • Examination of datasets and evaluation metrics for RRG applications.
  • Analysis of leading AI model performance, strengths, and limitations in RRG.

Main Results:

  • AI, particularly transformer and VLMs, shows promise in automating and improving RRG.
  • Key methods, architectures, and techniques in recent RRG advancements are highlighted.
  • Leading models demonstrate varying performance, with identified strengths and weaknesses.

Conclusions:

  • AI-powered RRG systems can enhance diagnostic speed and reduce radiologist workload.
  • Further research is needed to improve existing AI systems and explore new avenues in RRG.
  • Advancing AI capabilities in RRG can lead to better clinical decision-making and patient outcomes.