"Unfortunately, many conditions cause these common symptoms, and the diagnosis is not always straightforward.

"Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy emergency departments."

The current gold standard for diagnosing a heart attack is measuring levels of the protein troponin in the blood.

But the same threshold is used for every patient - even though levels are affected by age, gender and other health conditions.

Previous research has shown that women are 50% more likely to be incorrectly diagnosed at first. And people who are initially given the wrong diagnosis have a 70% higher risk of dying after 30 days.

But that could be prevented by the new algorithm, according to The British Heart Foundation, which funded the work.

CoDE-ACS worked well regardless of the patient's characteristics, according to the research published in the journal Nature Medicine.

It was developed with artificial intelligence based on data from more than 10,000 patients in Scotland.

It uses information including age, gender, ECG test results, medical history and troponin levels to predict the probability that someone has had a heart attack.

Professor Sir Nilesh Samani, medical director of the British Heart Foundation, said: "CoDE-ACS has the potential to rule-in or rule-out a heart attack more accurately than current approaches.

"It could be transformational for emergency departments, shortening the time needed to make a diagnosis, and much better for patients."

Professor Steve Goodacre, professor of emergency medicine at the University of Sheffield, called the study "intriguing", adding that it showed "how AI can use complex analysis, rather than a simple rule, to improve diagnosis".

"This doesn't [yet] show that we can replace doctors with computers," he added. "Experienced clinicians know that diagnosis is a complex business.

"Indeed, the 'ground truth' used to judge whether the AI algorithm was accurate was a judgement made by clinicians."