Luckily, foretelling such dire consequences may help doctors to stave them off. "Predicting the future of a patient is useful because it may enable doctors to tailor treatments to the individual," lead author Dr. Luke Oakden-Rayner told the University of Adelaide. "Instead of focusing on diagnosing diseases, the automated systems can predict medical outcomes in a way that doctors are not trained to do, by incorporating large volumes of data and detecting subtle patterns."

For this study, the system was looking for things like emphysema, an enlarged heart and vascular conditions like blood clotting.The deep learning system was trained to analyze over 16,000 image features that could indicate signs of disease in those organs. Machines have become adept at it surprisingly quickly, even though it's "something that requires extensive training for human experts," said Oakden-Rayner.

AI can pick problems in the heart and lungs (at left) that might lead to an early death.

The goal was not to build a grim diagnostic system, and the AI only analyzed retrospective patient data. Rather, the team is looking to lay the groundwork for algorithms that can diagnose your overall health, rather than just spotting a single disease. They also want to "motivate the use of routinely collected, high resolution radiologic images as sources of high quality data for precision medicine," according to the paper. In other words, they're encouraging more scans as a way to improve the results of future diagnostic systems.

"Our research opens new avenues for the application of artificial intelligence technology in medical image analysis, and could offer new hope for the early detection of serious illness, requiring specific medical interventions," says Oakden-Rayner.