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Medical artificial intelligence is a hugely appealing concept. In theory, models can analyze vast amounts of information, recognize subtle patterns in data, and are never too tired or busy to provide a response. However, although thousands of these models have been and continue to be developed in academia and industry, very few of them have successfully transitioned into real-world clinical settings.
Marinka Zitnik, associate professor of biomedical informatics in the Blavatnik Institute at Harvard Medical School, and colleagues are exploring why — and how to close the gap between how well medical AI models perform on standardized test cases and how many issues the same models run into when they’re deployed in places like hospitals and doctors’ offices.