Healthcare is one of the fundamental pillars of society. However, over the past 2 years, rapidly rising COVID-19 infection caseloads have placed global health systems under pressure, often threatening to overwhelm available resources. Now more than ever, the role of technology, specifically Artificial Intelligence (AI) solutions, in assisting medical practitioners and alleviating the extraordinary burden placed on them, is coming to the fore.
Qure.ai is a home-grown AI solutions provider that is disrupting the ‘status quo’ in diagnostics by enhancing imaging accuracy and improving health outcomes with the assistance of AI. We tap deep learning technology to automate interpretation of radiology exams for time and resource-strapped healthcare professionals, enabling faster diagnosis and speed to treatment. Our goal is to make healthcare more accessible and affordable to patients worldwide.
In mid-2020, we started working with secondary care hospitals in rural and semi-urban areas of India, that was delivering last mile healthcare connectivity. Some of these are in isolated tribal areas but host close to 100,000 OPD visits annually. With scarce resources, they were already handling a significant burden of Tuberculosis (TB), but had to swiftly respond to COVID-19 as well. Most did not have RT-PCR testing facilities nearby, increasing the diagnosis time by days, and exposing the limited staff to COVID-19.
Imaging procedures have been established to be the most effective method of evaluating the lung state of COVID-19 patients as well as measuring the disease’s rate of progression. The use of chest X-rays as a first investigation prior to confirmatory testing could be done because these hospitals have chest X-ray facilities. However, the scarcity of trained chest physicians and limited access to teleradiology services meant analysing chest X-rays could take days, leaving little time for reporting. We realised that our technology could help physicians in these hospitals in caring for vulnerable populations despite their resource constraints