The air ambulance industry plays a vital role in delivering fast, life-saving medical care to critically injured patients. However, deciding who needs transport to advanced trauma centers is a complex challenge. Thanks to recent research in Sweden, artificial intelligence (AI) may soon help solve this problem.
Why Fast and Accurate Decisions Matter
Air ambulance crews face high-stakes decisions in seconds. Research from Chalmers University of Technology, in collaboration with the University of Gothenburg and the University of Borås, highlights how quickly identifying severely injured patients improves survival rates. Patients taken directly to advanced trauma centers have a higher chance of recovery because those facilities are fully equipped to handle complex injuries.
Unfortunately, studies showed that 40% of severely injured patients were not sent directly to university hospitals, while 45% of those who were only mildly injured ended up in higher-level centers unnecessarily. These errors can stretch vital resources thin and delay the right care.
AI as a Life-Saving “Extra Colleague”
To tackle this, Swedish researchers developed five mathematical models using data from more than 47,000 real-life ambulance cases. These models analyzed variables such as blood pressure, respiratory rate, injury type, age, and gender. Results showed that the AI models outperformed the real-world decisions made by paramedics at the time of the incidents.
Anna Bakidou, a doctoral student on the project, explained that a decision-support AI system could act as an “extra colleague,” helping staff see complex patterns they might otherwise miss. She pointed out, for example, that younger accident victims are sometimes overestimated in terms of injury severity, while older patients—who might appear stable—can deteriorate rapidly due to internal injuries.
Challenges Before AI Can Take Off
Despite these promising results, researchers say integrating AI tools into everyday air ambulance operations will take time. Systems must be easy to use in stressful environments, possibly through voice controls so medics can keep their hands free. New tools would also need to blend seamlessly with current protocols and be continuously updated as new data emerges.
