Artificial Intelligence in Radiology - Advantages, Use Cases & Trends. Read about the challenges radiology AI is facing on the way to deployment
The radiology department of an average healthcare facility is likely searching for improvements. Even before COVID-19, 45% of radiologists experienced burnout at one point in their career. They felt overwhelmed with the administrative burden and the large number of images they had to check manually, which could reach up to a hundred scans per day. Additionally, radiology practice is lacking non-invasive methods for tissue classification. Invasive procedures take time and cause stress to patients.
Luckily, AI healthcare solutions are coming to the rescue. The global AI radiology market was valued at $21.5 million in 2018, and it is forecast to reach $181.1 million in 2025, growing at a staggering CAGR of 35.9%.
However, despite the numerous advantages of AI in radiology, there are still challenges preventing its wide deployment. How to properly train machine learning to aid radiology? Where does AI stand when it comes to ethics and regulations? How to make a strong business case for investing in artificial intelligence in radiology?
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What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
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