One of the core building blocks of the CDEI’s COVID-19 response is our repository - a database for novel use-cases of artificial intelligence and data specifically being used to counter and mitigate the effects of COVID-19 around the world.
The repository aims to highlight innovations that are yet to be fully explored, encouraging researchers, the media and policymakers to widen the scope of their analysis and pay attention to lesser known use cases. The fifth release of the repository can be found here.
In the coming weeks, we will be providing a six month retrospective of what we have learnt from producing the COVID-19 repository.
Key findings from the September repository
- Almost all (13 of 16) of this month’s entries were related to healthcare, with the majority of those specifically looking at use-cases in hospitals. Given that the UK faces an ongoing public health crisis and is entering a second-wave of coronavirus infections, it is not surprising that these use-cases are the most prevalent at this time.
- A portion of these use-cases are directly focused on the rapid detection and diagnosis of COVID-19. For example, CRISPR-based platforms are being employed to develop diagnostic tests as a scalable means to address disease detection, in a collaboration between US-based Mammoth Biosciences and GSK.
- We are seeing a proliferation of AI tools for decision-making support for clinicians, with a particular emphasis on ranking treatment for triage, to grapple with the backlog of appointments caused by COVID-19. DrDoctor’s tool automatically rates patients’ responses to digital questionnaires to algorithmically assess the urgency of their medical need, giving each patient a red, amber or green score. This is a trend we expect to continue, both in light of the size of the existing backlog, and the likelihood that it will increase under the pressure of the second-wave of the virus.
- Clinicians are being further supported by innovative data-sharing tools. These include: cloud-based technologies (e.g. a system where cellular chips in ventilation devices send data that is then sorted and made available to clinicians in an easy-to-read format), and automated real-time patient data (e.g. the Patient Status Engine which automates the collection of raw patient data and decision-support tools for clinicians, combining wearable sensors with wireless networks and big data to provide high-resolution patient monitoring).
- As social distancing remains an important behaviour in limiting the spread of the virus, we are seeing a concentration of tools being developed to measure and support it, in a variety of settings.
- Within hospitals, mixed-reality headsets are being trialled to minimise face-to-face contact. The technology ensures patients receive immediate access to specialist opinion on the ward, whilst allowing for physical distancing to be maintained.
- City centre mapping tools, such as howbusyistoon.com, are tracking busyness indicators (e.g. footfall in a given area and car park capacity) to equip citizens with the data they need to make decisions about whether social distancing will be possible in their local area.
- We are also continuing to see an acceleration of the use of existing technology in different fields, particularly healthcare. For example, hospitals are utilising chatbots to keep staff informed with the latest COVID-19 information, including a bilingual digital assistant in Wales.
About the CDEI
The CDEI was set up by government in 2018 to advise on the governance of AI and data-driven technology. We are led by an independent Board of experts from across industry, civil society, academia and government. Publications from the CDEI do not represent government policy or advice.