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After seeing hundreds of thousands of these kinds of images, the machine will start to see patterns correlated with the medical data inserted.

According to the Centers for Disease Control and Prevention, around 610,000 people die of heart disease in the United States every year, and 370,000 of these deaths are due to coronary heart disease (CHD).

With this alarming figure, the findings of Google AI is considered a breakthrough that could potentially save thousands of lives in the future.

While the news is definitely encouraging, Google cautions that more research needs to be done. "Our work also suggests avenues of future research into the source of these associations, and whether they can be used to better understand and prevent cardiovascular disease", conclude the authors of the study. In the testing phases, the algorithm was able to identify heart conditions 70% of the time, which is a slightly lower rate of success than the longer SCORE process which is correct around 72% of the time.

"They're taking data that's been captured for one clinical reason and getting more out of it than we now do", he said. "But we need to validate".

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Google has allegedly been working on predicting eye diseases using artificial intelligence for a while now. By studying their appearance with camera and microscope, doctors can infer things like an individual's blood pressure, age, and whether or not they smoke, all of which are crucial indicators of cardiovascular health.

Researchers at Google and Verily used machine learning to analyze data of almost 300,000 patients and accurately predicted cardiovascular risk factors using retinal images in two datasets of 12,026 and 999 patients. Maulik Majmudar, associate director of the Healthcare Transformation Lab at Massachusetts General Hospital, called the model "impressive" but noted that the results show how tough it is to make significant improvements in cardiovascular risk prediction. So, for example, if most patients that have high blood pressure have more enlarged retinal vessels, the pattern will be learned and then applied when presented just the retinal shot of a prospective patient.

Essentially Google has taken a diagnosis method with an established history, found new ways to analyze the data and sped it up significantly.

"I am very excited about what this means for discovery", Peng said. "Rather than replacing doctors, it's trying to extend what we can actually do". Google's AI was checked for accuracy against two independently collected data sets, consisting of eye imagery from 12,026 and 999 patients.