Reinforcement learning presents a mathematically robust method for optimal control, but its application in clinical anesthesia has not yet been reported.
No reported role in anesthesia practice; identifies a research gap requiring prospective validation before any clinical exploration.
Reinforcement learning (RL) is an intelligent systems technique with a history of success in difficult robotic control problems. Similar machine learning techniques, such as artificial neural networks and fuzzy logic, have been successfully applied to clinical control problems. Although RL presents a mathematically robust method of achieving optimal control in systems challenged with noise, nonlinearity, time delay, and uncertainty, no application of RL in clinical anesthesia has been reported.
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Moore et al. (2010) studied this question.
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