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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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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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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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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Revolutionizing Radiology With Artificial Intelligence.

Abhiyan Bhandari1

  • 1General Medicine, Wexham Park Hospital, Slough, GBR.

Cureus
|October 30, 2024
PubMed
Summary

Artificial intelligence (AI) enhances radiology by improving diagnostic accuracy and workflow efficiency. Addressing challenges like transparency and bias is key to realizing AI's full potential in patient care.

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly impacting healthcare, particularly in medical imaging.
  • AI offers potential advancements in diagnostic accuracy, workflow efficiency, and patient care within radiology.

Purpose of the Study:

  • To explore the impact of AI on various subfields of radiology.
  • To emphasize AI's potential to improve clinical practices and patient outcomes.

Main Methods:

  • Review of AI-driven technologies including machine learning, deep learning, and natural language processing (NLP).
  • Analysis of AI applications in radiology, such as computer-aided diagnosis (CAD) for image analysis and abnormality detection.
  • Integration of data from multiple imaging modalities (CT, MRI, PET) for comprehensive insights.
Keywords:
artificial intelligencediagnosispatient careradiologyworkflow optimization

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Main Results:

  • AI tools demonstrate high accuracy in analyzing medical images and detecting abnormalities like tumors.
  • AI facilitates personalized treatment planning and complements existing radiologist workflows.
  • AI automates routine tasks, aids in early disease detection, and supports clinical decision-making.

Conclusions:

  • AI holds significant promise for revolutionizing radiology by enhancing diagnostic capabilities and streamlining workflows.
  • Challenges such as algorithmic transparency, data privacy, and bias mitigation must be addressed for effective AI integration.
  • Collaboration among stakeholders and strong ethical guidelines are crucial for safe and effective AI implementation in clinical practice.