Artificial intelligence is being applied with undue haste to analyze data in some areas of biomedical research, leading to inaccurate findings, a leading US computer scientist and medical statistician warned on Friday.
"I would not trust a very large fraction of the discoveries that are currently being made using machine learning techniques applied to large data sets," Genevera Allen of Baylor College of Medicine and Rice University warned at the American Association for the Advancement of Science annual meeting.
Machine learning is a form of AI being applied widely to find patterns and associations within scientific and medical data, for example between genes and diseases. In precision medicine, researchers look for groups of patients with similar DNA profiles so that treatments can be targeted at their particular genetic form of disease.
"A lot of these techniques are designed to always make a prediction," Dr Allen said. "They never come back with 'I don't know' or 'I didn't discover anything' because they aren't made to."
She was reluctant to point a finger at individual studies but said uncorroborated discoveries from machine learning analysis of cancer data, published recently, were a good example.
"There are cases where discoveries aren't reproducible," Dr Allen said. "The clusters discovered in one study are completely different from the clusters found in another. Why? Because most machine-learning techniques today always say: 'I found a group'. Sometimes, it would be far more useful if they said: 'I think some of these are really grouped together, but I'm uncertain about these others.'"
From Financial Times
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