An algorithm that can spot cause and effect could supercharge medical AI. The technique, inspired by quantum cryptography, would allow large medical databases to be tapped for causal links
Researchers Anish Dhir and Ciarán Lee at Babylon Health, a UK-based digital health-care provider, have come up with a technique for finding causal relations across different data sets.
Babylon Health offers a chatbot-based app that asks you to list your symptoms before responding with a tentative diagnosis and advice on treatment. The aim is to filter out people who do not actually need to see a doctor. In principle, the service saves both patients’ and doctors’ time, allowing overworked health professionals to help those most in need.
But the app has come under scrutiny. Doctors have warned that it sometimes misses signs of serious illness, for example. … Babylon Health has singled itself out for criticism in part because of its overblown claims. For example, in 2018 the company announced that its AI could diagnose medical conditions better than a human doctor. A study in The Lancet a few months later concluded not only that was this untrue but that “it might perform significantly worse.”
Still, Dhir and Lee’s new work on causal links deserves to be taken seriously.