A new study by researchers at MIT
and Massachusetts General Hospital (MGH) suggests the day may be approaching when
advanced artificial intelligence systems could assist anesthesiologists in the operating room.
In a special edition of Artificial Intelligence in Medicine, the team of neuroscientists, engineers,
and physicians demonstrated a machine learning algorithm for continuously automating dosing of
the anesthetic drug propofol. Using an application of deep reinforcement learning, in which the
software’s neural networks simultaneously learned how its dosing choices maintain unconsciousness
and how to critique the efficacy of its own actions, the algorithm outperformed
more traditional software in sophisticated, physiology-based simulations of patients.
It also closely matched the performance of real anesthesiologists when showing
what it would do to maintain unconsciousness given recorded data from nine real surgeries.
The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness
with no more drug than is needed, thereby freeing up anesthesiologists for all the other
responsibilities they have in the operating room, including making sure patients remain immobile,
experience no pain, remain physiologically stable, and receive adequate oxygen.
The research team designed a machine learning approach that would not only learn how to
dose propofol to maintain patient unconsciousness, but also how to do so in a way that would
optimize the amount of drug administered. They accomplished this by endowing the software with
two related neural networks: an “actor” with the responsibility to decide how much drug to
dose at every given moment, and a “critic” whose job was to help the actor behave in a manner that
maximizes “rewards” specified by the programmer. For instance, the researchers experimented with
training the algorithm using three different rewards: one that penalized only overdosing,
one that questioned providing any dose, and one that imposed no penalties.
In every case, they trained the algorithm with simulations of patients that employed advanced
models of both pharmacokinetics, or how quickly propofol doses reach the relevant regions of
the brain after doses are administered, and pharmacodynamics, or how the drug
actually alters consciousness when it reaches its destination. Patient unconsciousness levels,
meanwhile, were reflected in measure of brain waves, as they can be in real operating rooms.
By running hundreds of rounds of simulation with a range of values for these conditions, both the
actor and the critic could learn how to perform their roles for a variety of kinds of patients.
The most effective reward system turned out to be the “dose penalty” one in which the critic
questioned every dose the actor gave, constantly chiding the actor to keep dosing to a necessary
minimum to maintain unconsciousness. Without any dosing penalty the system sometimes dosed
too much, and with only an overdose penalty it sometimes gave too little.
The “dose penalty” model learned more quickly and produced less error than the other value
models and the traditional standard software, a “proportional integral derivative” controller.
After training and testing the algorithm with simulations,
the researchers put the “dose penalty” version to a more real-world test
by feeding it patient consciousness data recorded from real cases in the operating
room. The testing demonstrated both the strengths and limits of the algorithm.
During most tests, the algorithm’s dosing choices closely matched those of the attending
anesthesiologists after unconsciousness had been induced and before it was no longer necessary.
The algorithm, however, adjusted dosing as frequently as every five seconds,
while the anesthesiologists typically did so only every 20-30 minutes
As the tests showed, the algorithm is not optimized for inducing unconsciousness in
the first place, the researchers acknowledge.
The software also doesn’t know of its own accord when surgery is over, they add,
but it’s a straightforward matter for the anesthesiologist to manage that process.
One of the most important challenges any AI system is likely to continue to face,
is whether the data it is being fed about patient unconsciousness is perfectly accurate.