Framing the challenges of artificial intelligence in medicine

On a clear January morning in Florida, a Tesla enthusiast and network entrepreneur was driving his new Tesla Model S on US Highway 27A, returning from a family trip. He had posted dozens of widely circulated YouTube tutorial videos on his vehicle and clearly understood many of the technical details of his car. That day, he let the vehicle run autonomously on Autopilot mode for 37 min, before it crashed into the trailer of a truck turning left. The Autopilot did not identify the white side of the trailer as a potential hazard, and the driver was killed, leaving his family and his high-tech business behind.1 This tragedy is not a metaphor for artificial intelligence (AI) applications but an example of a long-recognised challenge in AI: the Frame Problem.2 Although rarely appreciated in the scholarly and lay descriptions of the stunning recent successes of AI...

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