Building better diagnostic standards for medical AI (David Larson, Daniel L. Rubin, and Curtis Langlotz, Brookings)

As researchers grew to understand COVID-19 during the early days of the pandemic, many built AI algorithms to analyze medical images and measure the extent of the disease in a given patient. Radiologists proposed multiple different scoring systems to categorize what they were seeing in lung scans and developed classification systems for the severity of the disease. These systems were developed and tested in clinical practice, published in academic journals, and modified or revised over time. But the pressure to quickly respond to a global pandemic threw into stark relief the lack of a coherent regulatory framework for certain cutting-edge technologies, which threatened to keep researchers from developing new diagnostic techniques as quickly as possible.