Health tech has attracted the attention of a panoply of private companies, including giants like Amazon and Apple
trending, according to the app on Johannes Schildt’s phone. The app was created by Kry, the Swedish digital health-care firm Mr Schildt runs. It offers information on the sicknesses for which people are currently booking doctor’s appointments, as well as on things specifically important to its user—it keeps Mr Schildt, who suffers from hay fever, up to date with the pollen count.
Adding real-world data to genome-based profiles would undoubtedly be useful. Michael Joyner of the Mayo Clinic in Rochester, Minnesota, and Nigel Paneth at Michigan State University argue that characteristics such as family history, neighbourhood, socioeconomic circumstances, height and girth still outperform genetic profiling as predictors for all sorts of health outcomes. This does not mean genetic information is without value; it means it needs context.
One particular worry with machine learning in general is that bias in the “training sets” from which the computers learn their stuff can mean that the algorithms do not work equally well for all members of the population. Medical research has a poor historical record on such matters, for example when it does not match clinical-trial populations to the population at large, or excludes women of child-bearing age from trials. Machine learning could bake in such biases, and make them invisible.
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