Imperial College London

Professor Anthony Gordon

Faculty of MedicineDepartment of Surgery & Cancer

Chair in Anaesthesia and Critical Care



+44 (0)20 3312 6328anthony.gordon




ICUQueen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus






BibTex format

author = {Komorowski, M and Celi, LA and Badawi, O and Gordon, AC and Faisal, A},
doi = {10.1038/s41591-018-0213-5},
journal = {Nature Medicine},
pages = {1716--1720},
title = {The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care},
url = {},
volume = {24},
year = {2018}

RIS format (EndNote, RefMan)

AB - Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals1–3, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients1,4–6. To tackle this sequential decision-making problem, we developed a reinforcement learning agent, the artificial intelligence (AI) Clinician, which learns from data to predict patient dynamics given specific treatment decisions. Our agent extracted implicit knowledge from an amount of patient data that exceeds many-fold the life-time experience of human clinicians and learned optimal treatment by having analysed myriads of (mostly sub-optimal) treatment decisions. We demonstrate that the value of the AI Clinician’s selected treatment is on average reliably higher than the human clinicians. In a large validation cohort independent from the training data, mortality was lowest in patients where clinicians’ actual doses matched the AI policy. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.
AU - Komorowski,M
AU - Celi,LA
AU - Badawi,O
AU - Gordon,AC
AU - Faisal,A
DO - 10.1038/s41591-018-0213-5
EP - 1720
PY - 2018///
SN - 1078-8956
SP - 1716
TI - The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
T2 - Nature Medicine
UR -
UR -
VL - 24
ER -