This article is an installment of Future Explored, a weekly guide to world-changing technology. You can get stories like this one straight to your inbox every Thursday morning by subscribing here. Patient outcomes are almost always better when a disease is diagnosed and treated early, but some illnesses don't trigger symptoms until a patient is already really sick -- ovarian cancer, for example, can go undetected for 10 years or more, giving it time to spread to other organs. By screening healthy patients for these sneaky diseases, doctors can spot them earlier -- and new artificial intelligence (AI) tools promise to help in the hunt. The challenge: Cardiovascular diseases (CVDs) kill nearly 18 million people every year, making them the leading cause of death worldwide.

The research, described in Nature Biomedical Engineering, found that the model was more effective at identifying issues such as pneumonia, collapsed lungs, and lesions than other self-supervised AI models. In fact, it was similar in accuracy to human radiologists. While others have tried to use unstructured medical data in this manner, this is the first time a team's AI model has learned from unstructured text and matched radiologists' performance, and it has demonstrated the ability to predict multiple diseases from a given x-ray with a high degree of accuracy, says Ekin Tiu, an undergraduate student at Stanford and a visiting researcher who coauthored the report. "We are the first to do that and demonstrate that effectively in this field," he says. The model's code has been made publicly available to other researchers in the hope it could be applied to CT scans, MRIs, and echocardiograms to help detect a wider range of diseases in other parts of the body, says Pranav Rajpurkar, an assistant professor of biomedical informatics in the Blavatnik Institute at Harvard Medical School, who led the project.

But when it comes to how machine learning (ML) might benefit humanity, there's almost no field more promising than healthcare. Hardly a month passes when we don't hear about a new disease that machine learning models have learned to tag faster and more accurately than trained clinicians. ML is being used to help doctors spot tumors in medical scans, speed up data entry, and respond automatically to hospital patients' needs. These ML-powered breakthroughs come at a crucial time, as the shortage of doctors and specialists in the US and worldwide continues to grow. As our demand for doctors surpasses supply, we may well find ourselves depending on technology to help fill in the gaps.

Artificial intelligence developed by Google could soon be used to diagnose disease faster than doctors. The technology giant's AI firm, DeepMind, has a number of contracts with NHS hospitals to use its technology to improve detection and treatment of certain conditions from cancer to eye disease. Researchers have now submitted what it described as'promising' initial findings to a medical journal from a two-year project working with Moorfields Eye Hospital in London. It is thought that the technology, which has been programmed to detect signs of disease such as glaucoma, age related macular degeneration and diabetic retinopathy, could enter clinical trials within a couple of years. Retinal scans from thousands of patients were used to develop an algorithm - a set of mathematical instructions or rules that can work out answers to problems - to spot signs of disease.

Google's DeepMind division has announced a partnership with the NHS's Moorfields Eye Hospital to apply machine learning to spot common eye diseases earlier. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, with the aim to speed up the complex and time-consuming process of analysing eye scans. The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Two million people are already living with sight loss in the UK, of whom around 360,000 are registered as blind or partially-sighted.

Google DeepMind has announced its second collaboration with the NHS, as part of which it will work with Moorfields Eye Hospital in east London to build a machine learning system which will eventually be able to recognise sight-threatening conditions from just a digital scan of the eye. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, reports Ars Technica, with the aim to speed up the complex and time-consuming process of analysing eye scans. From the report:The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Two million people are already living with sight loss in the UK, of whom around 360,000 are registered as blind or partially-sighted.

Google's DeepMind division has announced a partnership with the NHS's Moorfields Eye Hospital to apply machine learning to spot common eye diseases earlier. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, with the aim to speed up the complex and time-consuming process of analysing eye scans. The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Mobile app called "Streams" provides medical staff with latest clinical information.