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London’s Air Ambulance Charity is looking into using artificial intelligence (AI) insights to improve pre-hospital care.
Together with Queen Mary University of London (QMUL), Barts Health and the University of Aberdeen, th charity is taking part in a £1.8m study which will support a clinical trial using the Artificial Intelligence in Trauma Risk Prediction System (AI-TRiPS) to aid clinical decision-making on the scene of an incident.
“Every second counts in the treatment of critically injured patients and tools like AI-TRiPS have the potential to provide our teams with life-saving insights in real time. This research represents a bold step forward in improving outcomes for our patients and London’s Air Ambulance is proud to be part of this ground-breaking study.”
Dr Zane Perkins, Consultant at London’s Air Ambulance, who is heavily involved in the study.
AI-TRiPs is an AI-powered, decision-support tool designed to assist in time-critical medical decision-making for severely injured patients, patients like the individuals our advanced trauma team attend to in London.
AI-TRiPS will be deployed across the London Trauma System, the largest integrated trauma network in the world, serving the 10 million people who visit, live and work in our capital city. It will involve trauma specialists from London’s four major trauma centres as well as our own doctors and paramedics, and representatives from London Ambulance Service.
Funded by the Congressionally Directed Medical Research Programs, researchers will evaluate the effectiveness of AI-TRiPs in supporting doctors assess life-threatening complications – such as severe blood loss – and helping them take action to improve outcomes.
If successful, this initiative could revolutionise trauma care worldwide and help save thousands of lives.
The AI algorithms, developed by trauma surgeons, military experts and computer scientists, integrate cutting-edge trauma research, registry data and clinical expertise. The system offers evidence-based predictions about the risks faced by critically injured patients and guidance on how best to manage these.
Importantly, the system explains the reasoning behind its predictions, ensuring transparency with an “open box” design that can be understood and explored by clinical users.
The clinical trial, supported by the University of Aberdeen’s Clinical Trials Unit, will begin with a year of development and regulatory approvals in January 2025. Patient recruitment will follow in early 2026, with results expected in 2027.
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