The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics, to be known as iCAIRD, brings together a pan-Scotland collaboration of 15 partners from across academia, the NHS, and industry.
On Tuesday, November 6 Greg Clark, UK Secretary of State for Business, Energy and Industrial Strategy (BEIS), announced that UK Research and Innovation will invest £10 million in iCAIRD as part of the Industrial Strategy Challenge Fund.
Partner companies in the University of Glasgow-led pan Scotland consortium, Canon Medical Research Europe Ltd and Philips, along with six SMEs, will provide more than £5 million of additional funding to support iCAIRD.
iCAIRD will be a Scottish centre of excellence and focus on the application of AI in digital diagnostics, ultimately enabling better and earlier diagnosis and more efficient treatment for patients. It is also predicted that iCAIRD will create new jobs centred around AI and digital technology in healthcare.
Centred at the University of Glasgow’s Clinical Innovation Zone at the Queen Elizabeth University Hospital, iCAIRD bring together teams across Scotland – in Aberdeen, St Andrews and Edinburgh – to enable joined-up academic and commercial technology development, alongside academic researchers locally and nationally.
The centre’s work will deliver significant benefits for patients through the development of solutions for more rapid treatment for stroke; expert chest x-ray reading; rapid and more accurate diagnosis in gynaecological disease and colon cancer; and partly automated mammogram analysis for breast cancer screening.
Making use of the capability of modern computers to process the large amounts of data gathered in NHS healthcare clinics, iCAIRD will also allow clinicians, health planners and industry to work together and ultimately solve healthcare challenges more quickly and efficiently, and in a way that completely protects patients’ identities.
Building on significant investment across Scotland, iCAIRD will integrate with Health Data Research UK (HDRUK) and the national Picture Archiving Communication System (PACS) for radiology.
Professor David Harrison, principal investigator for the project, based in St Andrews but with visiting professorships in both Glasgow and Edinburgh, said: “I am delighted that iCAIRD has been awarded £10m from Innovate UK. With our pan-Scotland approach, we will build on existing strengths and deploy AI within NHS Scotland to transform diagnostics and healthcare in Scotland to improve outcomes for patients.
“This is a genuine collaboration between researches from Scottish universities, the NHS, and industry partners who are also contributing large sums to enable this project to be a success. Our aim is to transform digital diagnostic healthcare in Scotland, in order to benefit patients and make processes more streamlined and modern for the NHS.”
Professor Dame Anna Dominiczak, vice principal and head of the College of Medical, Veterinary and Life Sciences at the University of Glasgow, said: “The formation of iCAIRD is a great coup for Scotland and its people, and further positions Scotland’s ability to be a global leader in Precision Medicine.
“iCAIRD epitomises our ‘triple helix’ approach to healthcare innovation and Precision Medicine by developing research and innovation concurrently in industry, the NHS and academia.
“By locating at the Clinical Innovation Zone at the Queen Elizabeth University Hospital, alongside partners in industry and the NHS, iCAIRD will also drive open innovation and encourage further industry collaborations, building upon existing expertise and infrastructure for driving AI-related healthcare and precision medicine innovation for the benefit of patients, NHS and the economy.”
Professor Sir Mark Walport, UK Research and Innovation chief executive, said: “Early diagnosis of illness can greatly increase the chances of successful treatment and save lives.
“The centres announced today bring together the teams that will develop artificial intelligence tools that can analyse medical images varying from x-rays to microscopic sections from tissue biopsies. Artificial intelligence has the potential to revolutionise the speed and accuracy of medical diagnosis.”
Founding iCAIRD partners are: Bering Ltd, Canon Medical Research Europe Ltd, Cytosystems Ltd, DeepCognito Ltd, Glencoe Software, HDRUK Scotland and Scotland’s National and Regional Safe Havens, Intersystems, Kheiron Medical Technology, NHS Grampian, NHS Greater Glasgow & Clyde, NHS National Services Scotland, NVidia, Philips, University of Aberdeen, University of Edinburgh, University of Glasgow (hub site of iCAIRD) and University of St Andrews.
Jeane Freeman, Scottish Cabinet Secretary for Health and Sport, said: “Innovation and technology is an absolutely essential part of our efforts to get quicker and more accurate diagnosis, improved treatment and better outcomes for patients.
“This successful bid is the culmination of a huge amount of hard work from many in the NHS, including NHS Grampian and NHS Greater Glasgow and Clyde, and the Scottish Government’s own Chief Scientist Office.
“It will complement some of the other ongoing work in Scotland in the field of health technology and life sciences, and I look forward to seeing some of the results and the benefits they will bring to patients.”
Business Secretary Greg Clark said: “AI has the potential to revolutionise healthcare and improve lives for the better. That’s why our modern Industrial Strategy puts pioneering technologies at the heart of our plans to build a Britain fit for the future.
“The innovation at these new centres will help diagnose disease earlier to give people more options when it comes to their treatment, and make reporting more efficient, freeing up time for our much-admired NHS staff time to spend on direct patient care.”
Ken Sutherland, president of Canon Medical Research Europe Ltd, said: “Canon Medical are excited to be part of the iCAIRD proposal and we look forward to working with the other partners in the consortium to create novel AI solutions that can be proven to deliver better healthcare outcomes for the people of Scotland.
“As such this project epitomises our Made for Life philosophy, a long standing commitment from Canon Medical towards our partners, patients, and you.
“Our strong focus on putting patients at the centre of everything we do drives our motivation to work with our customers and partners to meet this common goal and find new ways to develop intelligent solutions that meet growing needs to be more efficient and achieve results fast with increased clinical confidence.”
Marlon Thompson, general manager of Digital and Computational Pathology at Philips, said: “We look forward to working with the NHS and associated partners to accelerate the digitisation of pathology through our investment in this important initiative. This will be the foundation for advancing AI technologies that are urgently needed to address the major challenges facing pathologists today.
“The UK government has shown real leadership in funding these major collaborative centres. We share that vision at Philips and will leverage the full strength of our experience in digital and computational pathology to transform cancer services in the UK.”
Key iCAIRD priorities
- Create infrastructure to develop and apply AI in digital diagnostics, pathology and radiology.
- Fast-track digitisation of Scottish NHS pathology data to create the largest fully digital pathology laboratory network in Europe.
- Work with partner Canon to develop a network of Safe-Haven Artificial Intelligence Platforms (SHAIP), within existing NHS data ‘safe-havens’, first in Glasgow and then Aberdeen, eventually scaling nationwide, thus allowing crucially important research whilst ensuring protection of patients’ personal data.
- Work with partner Philips to establish an HDRUK national pathology image archive of anonymised disease cases within the National Data Safe Haven that can be used to train computers to augment and improve on current practice
- Work with industry to use SHAIP tool to apply and validate AI in stroke medicine, chest x-ray triage and mammogram interpretation, and apply AI to colon cancer data and gynaecological pathology.