What follows is the second part of our coverage of the "Radiology: Artificial Intelligence Fireside Chat" conducted at RSNA 2021. The in-depth discussion, for which excerpts are presented here, was well-facilitated by Dania Daye, MD, PhD, Massachusetts General Hospital/Harvard Medical School; and Paul Yi, MD., University of Maryland School of Medicine; with RSNA Journal Radiology: AI Editor Charles E. Kahn, Jr., MD, MS, Perelman School of Medicine, University of Pennsylvania. Featured panelists included: John Mongan, MD, PhD, University of California, San Francisco; Jayashree Kalpathy-Cramer, MS, PhD, Athinoula A. Martinos Center for Biomedical Imaging; and Linda Moy, MD, NYU Grossman School of Medicine. Q: The successes we have seen in AI are clear. There is cutting-edge research emerging, but with every success, we are identifying multiple obstacles.

The Morning Download delivers daily insights and news on business technology from the CIO Journal team. She joined information-technology executives from MGH, Gladstone Institutes and biotechnology company Ginkgo Bioworks on a virtual panel about Covid-19 research, which was hosted Thursday by data storage company VAST Data Inc. It was important to have large amounts of data storage, easy access to data and enough computational power to build complex AI models, Dr. Kalpathy-Cramer said. Researchers from various task forces at MGH have come together over the past several months to use AI algorithms in a number of ways, she said. They are using AI models to predict which Covid-19 patients will require more advanced treatments and to estimate how many intensive-care unit beds might be needed at a given time, Dr. Kalpathy-Cramer said.

Artificial intelligence is only getting more powerful. Today, we're able to build computer programs that can master complex strategy games and even hold natural-feeling conversations. A new study on a specialized AI algorithm found that it was able to automatically diagnose a disease that causes childhood blindness more accurately than trained physicians can, a step towards automating medical tasks that are often bottle-necked by a shortage of doctors. The algorithm was developed and studied by scientists at Oregon Health and Science University and Massachusetts General Hospital. It was trained to diagnose a disease called retinopathy of prematurity (ROP) that, if untreated, will lead to total blindness.

Michael Chiang reviews images of the eyes of premature babies who were sent to him via the telemedicine program at OHSU for ROP monitoring and diagnosis. An artificial intelligence system is better than most experts at diagnosing a childhood blindness disease, according to a new study from Oregon Health and Science University. Retinopathy of prematurity, or ROP, is a disease that can cause blindness in premature babies. It's how the musician Stevie Wonder lost his sight. To diagnose ROP, doctors have to examine the back of the eye and see whether the blood vessels are overly dilated or wiggly.

Doctors can prevent one of the most common causes of blindness in young children -- but only when they can detect it. The disease, called retinopathy of prematurity, or ROP, affects the youngest, smallest and most vulnerable infants. These are preterm babies born before 31 weeks who weigh less than 2¾ pounds. Doctors can treat ROP if they catch it early enough, but there's no objective way to determine which cases need treatment. Jayashree Kalpathy-Cramer thinks AI can make a difference.