Artificial intelligence has long been improving diagnoses

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But recently the field has exploded

a routine mammogram in January 2023. A few weeks later she was asked to visit her doctor in the Aberdeen Royal Infirmary, in Scotland. The mammogram had looked fine to two doctors, but ansystem called Mia had seen something amiss: a six-millimetre patch of subtly-off shades of grey. It was stage 2 cancer. Had it not been spotted at that point and removed, it would not have been caught until Barbara came in for her next routine screen—or until it made its presence known in some other way.

Neural networks, inspired by the structure of the brain’s visual cortex, are systems in which information flows through layers of “neurons” stacked one on top of the other. In early neural networks all the neurons in one layer were connected to all the neurons in the next. AlexNet was a “convolutional” neural network—one in which the connections were more sparse, something that allows more discriminate forms of analysis.

As a paediatric neurologist, Sharief Taraman says he can expect to see thousands of children; but thewhich his Silicon Valley based company, Cognoa, has built to assess children for autism has been trained on footage of hundreds of thousands. As a result it can use video uploaded by parents, along with a questionnaire, to reach an assessment of their condition.

In 2019 a systematic review of the diagnostic accuracy of 82 algorithms in medical imaging found that the ways in which they had been assessed were often sub par. A particular worry was a lack of “prospective” trials which look at outcomes after an intervention, as opposed to retrospective trials which begin with outcomes and go back to look at what went before.

 

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