Introduction: Artificial intelligence (AI) will bring changes to the professional life of radiologists,
as well as changing many other aspects of our lives. Since the invention of
electricity, the internet and, more recently, artificial intelligence, the
technologies of general use have made it possible for societies to progress and
improve their quality of life.
Artificial
intelligence and machine learning tools have the potential to analyze large
data sets and extract meaningful information to improve patient outcomes, a
skill that is also useful in radiology and pathology.
The
images obtained by the MRI machines,
the computed tomography (CT) scanners, and the radiographs, as well as the biopsy
samples, allow the doctors to see the internal functioning of the human body.
Factor
Affecting
- Abundance of data
- Development of artificial neural networks
- Increased affordability of the hardware
- The future of radiology augmented with Artificial Intelligence
Radiologists
are not familiar with Artificial Intelligence, pioneering work in the
perception of medical images in the 80s. We are experts in domains in medical
imaging, medical physics, and radiation safety. But in the last 6 to 12 years,
there have been substantial innovations in obtaining images from deep learning
methods of image classification. Today's artificial neural networks have rates
of accuracy that surpass those of human radiologists in narrow-based tasks,
such as nodule detection The first step in formulating a strategy is to define
our capabilities and identify the competitive forces that represent a threat.
We are facing competition from other medical specialties who spend more time
interacting with patients and who can choose to buy AI technologies. We also
face competition from suppliers of equipment that make imaging devices, such as
CT scanners.
General
use cases, potential impact and implementation strategy
They
can be divided into task-based categories:
- Detection and prediction automation
- Intelligence augmentation
- Precision diagnostics and big data
- Radiological decision support systems
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