In 2012, IBM Watson went to medical school. So said The New York Times, announcing that the tech giant's artificially intelligent question-and-answer machine had begun a "stint as a medical student" at the Cleveland Clinic Lerner College of Medicine. This was just a metaphor. Clinicians were helping IBM train Watson for use in medical research. But as metaphors go, it wasn't a very good one.
At Harvard Business School (HBS), MBA students are pondering a future when robots rule the road. The pioneers of the driverless car movement -- such as Google and Tesla -- are mapping the MBAs a future in which artificial intelligence and robotics will likely impact the entire job market and global economy. David Yoffie, professor of international business administration at HBS, believes such disruptive technologies are now an "essential" part of the b-school landscape. "What I'm trying to teach students is: What can these technologies deliver? And what are the challenges and opportunities for a company that does AI?" he says.
In a Harvard Business School classroom in Boston, MA, robots are on the rise. MBA students are trying to crack a case study on the self-driving cars pioneered by Tesla, Google, and Uber. What is the potential for robots to reshape our roads? And what are the challenges and opportunities of entering that business? This is a case that David Yoffie, professor of international business administration, believes is essential reading for tomorrow's business leaders.
Medical imaging is expected to be one of the early useful applications of artificial intelligence and machine learning in healthcare. And a slew of deals have been built around that premise in the last year or so--IBM Watson Health bought cloud-based imaging company Merge for 1 billion; Philips partnered with Hitachi to incorporate AI into its image management; and GE added deep learning software from startup Arterys to its cardiac imaging. Now, another major cloud-based imaging startup is working to incorporate machine learning, first into X-ray analysis and eventually into other imaging modalities including CT and MRI. The Goldman Sachs-backed startup Imaging Advantage, which reportedly tapped into up to 250 million in debt in January 2015, has partnered with the Massachusetts Institute of Technology as well as Harvard Medical School and Massachusetts General Hospital to develop an artificial intelligence engine known as Singularity Healthcare. The result is expected to launch this quarter.
Last June, a team at Harvard Medical School and MIT showed that it's pretty darn easy to fool an artificial intelligence system analyzing medical images. Researchers modified a few pixels in eye images, skin photos and chest X-rays to trick deep learning systems into confidently classifying perfectly benign images as malignant. These so-called "adversarial attacks" implement small, carefully designed changes to data--in this case pixel changes imperceptible to human vision--to nudge an algorithm to make a mistake. That's not great news at a time when medical AI systems are just reaching the clinic, with the first AI-based medical device approved in April and AI systems besting doctors at diagnosis across healthcare sectors. Now, in collaboration with a Harvard lawyer and ethicist, the same team is out with an article in the journal Science to offer suggestions about when and how the medical industry might intervene against adversarial attacks.