Robert Chang, a Stanford ophthalmologist, normally stays busy prescribing drops and performing eye surgery. But a few years ago, he decided to jump on a hot new trend in his field: artificial intelligence. Doctors like Chang often rely on eye imaging to track the development of conditions like glaucoma. With enough scans, he reasoned, he might find patterns that could help him better interpret test results.
That is, if he could get his hands on enough data. Chang embarked on a journey that’s familiar to many medical researchers looking to dabble in machine learning. He started with his own patients, but that wasn’t nearly enough, since training AI algorithms can require thousands or even millions of data points. He filled out grants and appealed to collaborators at other universities. He went to donor registries, where people voluntarily bring their data for researchers to use. But pretty soon he hit a wall. The data he needed was tied up in complicated rules for sharing data. “I was basically begging for data,” Chang says.
