AI has been developed that can predict when you will die more accurately than a doctor as it can see things human's can't.

Researchers set up the AI at the healthcare company Geisinger to calculate the survival chances of heart patients.

It is designed to predict a patient’s chances of dying within a year – and has ended up doing it more accurately than doctors.

The AI predicts patients deaths by studying 1.8 million heart scan ECG results from 400,000.

The team found it has outperforming traditional prediction methods used by medics.

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The AI was asked to predict the chances of a patient dying within the next year or suffering further complications.

It was found to always outperform the model used on the data accumulated by doctors.

“No matter what, the voltage-based model was always better than any model you could build out of things that we already measure from an ECG,” Brandon Fornwalt of Geisinger told New Scientist.

The AI achieved a 0.85 score – with one being a perfect score.

This compared traditional methods by doctors which achieve somewhere between 0.6 and 0.8.

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The AI also accurately predicted the deaths of people who doctors believed where “healthy” based on results from their ECG.

Geisinger said that three doctors who separately reviewed "normal-looking" ECGs couldn’t pick up on what pattern the AI was picking up.

Fornwalt said: “That finding suggests that the model is seeing things that humans probably can’t see, or at least that we just ignore and think are normal.

“AI can potentially teach us things that we’ve been maybe misinterpreting for decades.”

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The AI uses the voltage from ECGs after being trained on two models, one including raw data and one including the age and sexes of patients.

It comes as the FDA in the US set out regulations around the use of AI in healthcare.

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They said they “expect a commitment from manufacturers on transparency and real-world performance monitoring”.

However, the FDA added that AI has the “potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day”.

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