The self-learning model identifies drug regimens that shrink tumors while minimizing side effects
AlphaGo became the first household AI name by teaching itself to play the ancient Chinese game Go and then beating the world’s best human player. Self-driving cars use AI systems to learn to park or merge into traffic by practicing the maneuvers over and over until they get it right.
It’s clear that AI programs are good at training themselves to win, maximize, or perfect. But what if success means striking a balance?
In cancer treatments, doctors endeavor to dose patients with enough drugs to kill as many tumor cells as possible but as few patient cells as possible. In other words, they balance shrinking a tumor with minimizing side effects.
“We said, ‘Wait. This sounds like a machine-learning search problem and optimization issue,’ ” says Pratik Shah, an MIT Media Lab principal investigator. “We thought we could do something to understand the process better.”
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