AI in Medicine Needs to be Carefully Deployed to Counter Bias – and Not Entrench It
upstart writes:
Doctors, data scientists and hospital executives believe artificial intelligence may help solve what until now have been intractable problems. AI is already showing promise to help clinicians diagnose breast cancer, read X-rays and predict which patients need more care. But as excitement grows, there's also a risk: These powerful new tools can perpetuate long-standing racial inequities in how care is delivered.
"If you mess this up, you can really, really harm people by entrenching systemic racism further into the health system," said Dr. Mark Sendak, a lead data scientist at the Duke Institute for Health Innovation.
These new health care tools are often built using machine learning, a subset of AI where algorithms are trained to find patterns in large data sets like billing information and test results. Those patterns can predict future outcomes, like the chance a patient develops sepsis. These algorithms can constantly monitor every patient in a hospital at once, alerting clinicians to potential risks that overworked staff might otherwise miss.
The data these algorithms are built on, however, often reflect inequities and bias that have long plagued U.S. health care. Research shows clinicians often provide different care to white patients and patients of color. Those differences in how patients are treated get immortalized in data, which are then used to train algorithms. People of color are also often underrepresented in those training data sets.
"When you learn from the past, you replicate the past. You further entrench the past," Sendak said. "Because you take existing inequities and you treat them as the aspiration for how health care should be delivered."
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