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?

Web scraping (data search and extraction from HTML-pages) has become a real gem for digital marketers and analysts that need to collect massive data sets for further research. This blog post contains 75 real-life scraping cases and a cheat sheet with regexp for the most popular sites like Google, Amazon, Facebook, etc.

It's been a decade since it was first discovered that Google has famously tested those 41 shades of blue. Last month I discovered that they began experimenting with link colors on their search results screen - once again.