Will AI Save Lives?

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Artificial intelligence trained on health records can now detect kidney injury up to two days before it occurs. The idea is that an advance warning could help doctors intervene earlier to prevent irreversible damage to the kidneys.

AIs are already touted as rivals to doctors when it comes to detecting medical conditions such as certain cancers and childhood illnesses. But few undergo rigorous clinical trials, so it’s still too early to know whether they are effective in practice.

Nenad Tomašev at DeepMind and colleagues trained an algorithm to predict the likelihood that a person who was admitted to hospital would go on to develop acute kidney injury (AKI).

They trained the AI using de-identified electronic health records from 703,782 US veterans aged between 18 and 90, who were admitted to hospital between October 2011 and September 2015.

Read more: AIs that diagnose diseases are starting to assist and replace doctors

AKI results in a dramatic drop in the rate at which the kidneys filter blood. This causes a decrease in urine production and a build-up of waste products in the blood, such as creatinine, a by-product of muscle breakdown. Both of these are used as measures for diagnosis.

Based on creatinine levels from a patient’s medical records, at a given time point the AI predicted whether a kidney injury would occur within the next 48 hours. Its accuracy was confirmed by comparing the prediction to whether the patient was later diagnosed.

The algorithm was fairly accurate at predicting the most severe forms of AKI. It correctly predicted 90 per cent of the cases in which the patient’s kidney function deteriorated so severely that they eventually required long-term dialysis.

It is difficult for doctors to anticipate kidney injury, so that level of accuracy is significant given the consequences of a severe injury, which include death or the need for a kidney transplant, says Eric Topol at Scripps Research in the US, who was not involved in the research.

However, the algorithm was far less accurate for all forms of AKI, correctly predicting only 55.8 of all episodes, with a ratio of two false alerts for one correct prediction. .... "