Consultant in Emergency Medicine and Co Director of EmQuire the QEUH ED Research Group. He leads a portfolio of projects centred on unscheduled care and specifically data, devices and decisions. His research focuses on decision-making within acute care and the interface between data, the clinician and the patient. He is the clinical lead for data driven solutions for a range of conditions including trauma, COPD and chest pain to support and enhance clinical decision-making and operational efficiency. Machine Learning work includes hospital flow management, imaging and risk stratification funded by InnovateUK, ScotGov and Industry.
Artificial intelligence (AI) and machine learning are revolutionizing our daily lives. Algorithms are increasingly embedded in many devices surrounding us and following us wherever we go. Even in our sleep, data can be streamed from various sensors and analyzed to wake us up at the optimal time. While some industries - such as finance, retail and manufacturing - have already made great progress in putting these algorithms to work, we’re now only starting to witness success stories emerging from the healthcare industry as well. This session will present 3 use cases demonstrating the impact of AI and machine-learning in real-world clinical settings and share the lessons learned from their implementation processes. This session will also encourage an interactive exchange of innovative ideas and best practices.