Is Google breast cancer detection AI better than doctors? Not so fast ZDNet

How much credit do you get if you're "pretty right" -- meaning, more right than wrong? If you're an artificial intelligence algorithm, you're given a lot of credit. AI programs don't have to have a definitive answer, just a probabilistic one, a percentage likelihood of the right answer, whether the task is performing natural-language translation or diagnosing cancer. The latest example of AI's probabilistic achievements is in this week's issue of Nature magazine, titled "International evaluation of an AI system for breast cancer screening," and is authored by an army of 31 scholars from Google's Google Health unit, its DeepMind unit, and the Imperial College of London, led by authors Scott Mayer McKinney, Marcin T. Sieniek, Varun Godbole, and Jonathan Godwin (DeepMind CEO Demis Hassabis is among the authors). In addition, a blog post gives commentary by Google's Google Health scholars Shravya Shetty, M.S., and Daniel Tse, M.D. Google's Google Health team, its DeepMind unit, and London's Imperial College used a trio of three different deep learning neural networks, consisting of, from the top, Facebook AI's "RetinaNet," combined with Google's "MobileNetV2," followed by the now standard ResNet-v2-50 in the middle section, and lastly a ResNet-v1-50 on the bottom layer.