It’s one thing to train an algorithm to produce headline-grabbing results on paper. It’s quite another to train it to improve care for patients in practice. STAT reporter Casey Ross explores the challenges health systems must address to close that crucial gap in the field of artificial intelligence, where the science of validating algorithms is still a work in progress.
It is work that involves accounting for impacts on costs, clinician routines, and the innumerable variables presented by patients’ needs and experiences. Ultimately it requires definitively answering the core question that most people in medicine are still asking: Will AI really help people once it is unleashed into a world much more complicated than the carefully curated data sets on which it was trained?
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