Nurse Anesthetists

Right now, nearly all these tasks are still done by people. For example, disassembling and cleaning an anesthesia machine is done by a nurse or tech (hospitals do use automated washers for general medical instruments ^[1], but anesthesia equipment parts go through manual cleaning). Anesthesia machines can run self-checks, but a person still calibrates sensors and fixes problems.

Likewise, nurses choose and give anesthesia medications – there are no systems that fully replace that decision-making. Researchers have tested “closed-loop” drug pumps (which automatically adjust drug levels) and found they can keep patients at the target dose more steadily ^[2], but these pumps are still experimental and not in everyday use ^[2]. Some AI tools exist to help clinicians – for example, an algorithm can predict if a patient’s blood pressure may drop soon – but these just alert the team; the nurse still decides how to respond.

In one review of AI in anesthesia, authors noted that anesthetists “continue to rely on their own clinical judgment, often without AI support” ^[2]. Importantly, tasks that require human care – like inserting an arterial line, interviewing a patient, exam­ining them, or deciding when to discharge them – have no automation. In short, none of the listed tasks is currently handled by a robot on its own.

In fact, an industry report finds that only 4 out of 521 FDA-approved AI medical devices in 2022 were for anesthesiology ^[2], which shows automation in this field is still just beginning.

Why might AI tools in anesthesia spread quickly or slowly? A big reason for slow adoption is safety and evidence. Doctors and hospitals will only use new tech if it clearly helps patients.

So far, studies of automated anesthesia systems are still few and mixed. For instance, one meta-analysis found that using AI-based monitors (like a pressure-prediction tool) did not significantly change patient outcomes ^[2]. The same review concluded that AI in anesthesiology “is in its infancy” ^[2].

Medical experts also point out that although closed-loop pumps can perform well in trials, they “have not yet been implemented on a large scale” because of strict regulations and clinician caution ^{[2] [2]}. In other words, anesthesiology must meet very high safety standards, so new devices need plenty of testing. Money matters too: hospitals will compare the cost of an AI device to hiring a trained nurse.

Right now there are very few anesthesia-specific AI tools approved ^[2], so widespread use will have to build slowly.