Artificial intelligence is advancing rapidly in healthcare, but what could it mean for pre-hospital care? In this compilation episode, we bring together two conversations exploring how AI could support clinicians, improve decision-making, and transform the way emergency care is delivered.
We begin with Zane Perkins, exploring AI-TRiPS, an AI Risk Prediction and Decision Support System for trauma care. We discuss how AI and Bayesian networks could support clinical reasoning, identify risk and reduce cognitive burden in high-pressure environments such as major trauma and air ambulance care.
But with that potential comes important questions around trust, cognitive bias, human factors and clinical responsibility. What happens when clinicians disagree with an algorithm? And could we become too reliant on technology?
We then broaden the discussion with Nico Preston, exploring the wider applications of AI across pre-hospital care, from dispatch and demand prediction to diagnostics, risk stratification and clinical decision support.
Across both conversations, one theme becomes clear: the future of AI in emergency care is unlikely to be about replacing clinicians. Instead, the opportunity may be to create human–AI systems that combine computational power with clinical experience, judgement and contextual awareness. But getting that balance right will be critical. Can AI make us better clinicians, and how do we ensure we use it safely?
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