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Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

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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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Lawman Chiwome¹, Onosetale M Okojie², A K M Jamiur Rahman³

¹General Internal Medicine, University Hospitals of Morecambe Bay NHS Foundation Trust, Lancaster, GBR.

|August 8, 2020

Summary

Artificial Intelligence (AI) offers significant potential in mammogram interpretation for breast radiology. This review explores AI

Keywords:

artificial intelligence breast cancer mammogram radiology

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Published on: August 30, 2013

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Breast cancer is a leading cause of mortality and morbidity in the UK, placing a significant burden on the National Health Service (NHS).
The NHS breast cancer screening program has improved survival rates but increased radiologist workload due to more mammograms and double reading.
Previous computer-aided detection (CAD) systems did not meet expectations for improving reader performance, leading to a search for more advanced solutions.

Purpose of the Study:

To review the current literature on Artificial Intelligence (AI) applications in breast radiology.
To explain the fundamental principles and terminology of AI in the context of radiology.
To analyze the potential uses and limitations of AI in mammogram interpretation and address whether AI will replace radiologists.

Main Methods:

Systematic review of articles on AI in breast radiology.
Analysis of AI applications, principles, and terminology.
Evaluation of AI's impact on mammogram interpretation and radiologist roles.

Main Results:

AI shows promise in mammogram interpretation, with a surge in research publications.
Existing CAD systems have limitations, driving the development of AI and machine learning applications.
AI has numerous applications in radiology, but its role relative to radiologists is still under investigation.

Conclusions:

AI has the potential to significantly impact breast radiology, particularly in mammogram interpretation.
While AI offers advanced capabilities, its role in replacing or augmenting radiologists requires further study.
Understanding AI principles, uses, and limitations is crucial for future radiologists and current practitioners.