"When my father was misdiagnosed and administered the wrong medication placing him in a coma nearly 20 years ago, I saw firsthand the need for technology to help physicians make accurate decisions," said Tanveer Syeda-Mahmood, IBM Fellow and Chief Scientist of the Medical Sieve Radiology Grand Challenge Project at IBM Research – Almaden in San Jose, Calif. This week in Chicago, Dr. Syeda-Mahmood's mission meets the real world as IBM Research debuts a new Watson-powered demo that shows the future of Artificial Intelligence (AI) in radiology. The demo is the result of a shared vision by Dr. Syeda-Mahmood and Dr. Eugene Walach from IBM Research – Haifa to help radiologists make accurate patient diagnoses quickly and easily. In any given day, radiologists can review up to thousands of medical images to make health diagnoses. To date, accuracy has relied mainly on medical professionals piecing together multiple sources of clinical information visually and manually to make critical decisions, including electronic health records, research publications and other data.

It's not every day that you get to watch artificial intelligence in action. IBM Watson Health and the Radiological Society of North America teamed up on a project called "Eyes of Watson," on display at the RSNA conference this week in Chicago. The demonstration is meant to show radiologists how Watson evaluates X-ray images, from start to finish. When I stopped by earlier this week, the area was busy with people interested to compare what they saw in a scan to what Watson saw. To start, I was asked to pick which finding I saw in a particular image of a breast.

Most smart software in use today specializes on one type of data, be that interpreting text or guessing at the content of photos. Software in development at IBM has to do all those at once. It's in training to become a radiologist's assistant. The software is code-named Avicenna, after an 11th century philosopher who wrote an influential medical encyclopedia. It can identify anatomical features and abnormalities in medical images such as CT scans, and also draws on text and other data in a patient's medical record to suggest possible diagnoses and treatments.