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July 19, 2020 03:34 pm

Statisticians Warn That AI Is Still Not Ready To Diagnose COVID-19

Discover magazine reports:For years, many artificial intelligence enthusiasts and researchers have promised that machine learning will change modern medicine. Thousands of algorithms have been developed to diagnose conditions like cancer, heart disease and psychiatric disorders. Now, algorithms are being trained to detect COVID-19 by recognizing patterns in CT scans and X-ray images of the lungs. Many of these models aim to predict which patients will have the most severe outcomes and who will need a ventilator. The excitement is palpable; if these models are accurate, they could offer doctors a huge leg up in testing and treating patients with the coronavirus. But the allure of AI-aided medicine for the treatment of real COVID-19 patients appears far off. A group of statisticians around the world are concerned about the quality of the vast majority of machine learning models and the harm they may cause if hospitals adopt them any time soon. So far, their reviews of COVID-19 machine learning models aren't good: They suffer from a serious lack of data and necessary expertise from a wide array of research fields. But the issues facing new COVID-19 algorithms aren't new at all: AI models in medical research have been deeply flawed for years... "The main problems appear to be (perhaps unsurprisingly) a lack of necessary data, and not enough domain expertise," writes Slashdot reader shirappu:The leader of the above group of statisticians, Maarten van Smeden, pointed to a lack of collaboration between researchers as a road block in the way of developing truly accurate models. "You need expertise not only of the modeler," he said, "but you need statisticians, epidemiologists and clinicians to work together to make something that is actually useful." One biostatistician has even been arguing that with many current AI models, medical researchers are using machine learning to "torture their data until it spits out a confession."

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