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 healtchare 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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