![]() ![]() Many of the problems that were uncovered are linked to the poor quality of the data that researchers used to develop their tools. “The pandemic has put problems in the spotlight that we’ve been dragging along for some time,” says Wynants. So what went wrong? And how do we bridge that gap? If there’s an upside, it is that the pandemic has made it clear to many researchers that the way AI tools are built needs to change. “I fear that they may have harmed patients,” says Wynants. Wynants and Driggs both say that a few of the algorithms they looked at have already been used in hospitals, and some are being marketed by private developers. Unrealistic expectations encourage the use of these tools before they are ready. ![]() “There is a lot of hype about machine-learning models and what they can do today,” says Driggs. ![]() But they are concerned that it could be harmful if built in the wrong way because they could miss diagnoses or underestimate risk for vulnerable patients. Wynants and Driggs still believe AI has the potential to help. ![]()
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