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NEWS: Machine learning success claimed for primary care diagnosis

by in News

New Artificial Intelligence symptom checkers could help reduce diagnostic mistakes in primary care, the scientists behind a controversial on-line GP company have reported.

The new approach overcomes the limitations of earlier versions by using causal reasoning, according to a study reported in Nature Communications. Previously, diagnoses were based solely on correlations between symptoms and the most likely cause, according to the researchers at Babylon Health. Writing in Nature Communications yesterday, Dr Jonathan Richens and colleagues of Babylon Health outline their new approach, causal machine learning. It includes the ability to “imagine” the possibility of a patient’s symptoms being due to a range of different conditions.

Dr Richens says: “We took artificial intelligence with a powerful algorithm, and gave it the ability to imagine alternate realities and consider ‘would this symptom be present if it was a different disease’? This allows the artificial intelligence to tease apart the potential causes of a patient’s illness and score more highly than over 70% of the doctors on these written test cases.”

This method could provide diagnoses in regions where access to doctors is limited, Babylon chief executive Dr Ali Parsa said. He said: “Half the world has almost no access to health care. So it’s exciting to see these promising results in test cases. This should not be sensationalised as machines replacing doctors, because what is truly encouraging here is for us to finally get tools that allow us to increase the reach and productivity of our existing health care systems.

“Artificial intelligence will be an important tool to help us all end the injustice in the uneven distribution of health care, and to make it more accessible and affordable for every person on Earth.”

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