Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Clinical Senior Lecturer







5L15Lab BlockCharing Cross Campus





Matthieu Komorowski MD PhD is a Clinical Senior Lecturer in the department of Surgery and Cancer at Imperial College London and an honorary consultant in intensive care and anaesthetics at Charing Cross Hospital. He holds full board certification in anaesthetics and intensive care in both France and the UK. He was previously a research fellow at the European Space Agency and holds additional qualifications in space, mountain, diving and hyperbaric medicine.

He joined Imperial College London in 2014 and completed a Masters of Research and a PhD in Medicine and Bioengineering, supervised by Profs Aldo Faisal and Anthony Gordon. In 2016/2017, he was a visiting scholar at the Laboratory of Computational Physiology at Harvard-MIT Division of Health Sciences and Technology (Profs Roger Mark and Leo Celi).

In his research, he applies machine learning techniques to build the next generation of decision support systems for critical care with a specific focus on sepsis.



Thierry S, Jaulin F, Starck C, et al., 2023, Evaluation of free-floating tracheal intubation in weightlessness via ice-pick position with a direct laryngoscopy and classic approach with indirect videolaryngoscopy., Npj Microgravity, Vol:9, ISSN:2373-8065

Cheung HC, De Louche C, Komorowski M, 2023, Artificial Intelligence Applications in Space Medicine., Aerosp Med Hum Perform, Vol:94, Pages:610-622

Komorowski M, Del Pilar Arias López M, Chang AC, 2023, How could ChatGPT impact my practice as an intensivist? An overview of potential applications, risks and limitations., Intensive Care Med, Vol:49, Pages:844-847

Pan P, Komorowski M, Shen L, et al., 2023, Editorial: Clinical teaching and practice in intensive care medicine and anesthesiology, Frontiers in Medicine, Vol:10, ISSN:2296-858X, Pages:1-2

Smit JM, Krijthe JH, van Bommel J, et al., 2023, The future of artificial intelligence in intensive care: moving from predictive to actionable AI, Intensive Care Medicine, ISSN:0342-4642

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