AI in Anesthesiology

Anesthesiologists watch a stream of numbers during surgery: blood pressure, heart rate, oxygen saturation, exhaled gases, and often a processed brain signal such as the bispectral index (BIS). AI is being applied in three main areas: predicting problems, automating drug delivery, and assisting procedures. The best-known prediction tool is the Hypotension Prediction Index (HPI) from Edwards Lifesciences, which received FDA marketing authorisation in 2018. It analyses the shape of the arterial pressure waveform and gives a score from 0 to 100 estimating the chance that mean arterial pressure will drop below 65 mmHg within the next few minutes. Low blood pressure during surgery is associated with acute kidney injury and heart damage, so earlier warning could let clinicians give fluids or vasopressors sooner. A small randomised trial, HYPE, published in JAMA in 2020, found patients spent less time hypotensive when HPI guided care. Later analyses questioned how much HPI adds beyond closely watching current blood pressure, and larger studies have given mixed results, so benefit on patient outcomes is not settled. Closed-loop systems go further and adjust infusions automatically. Research systems have titrated propofol against BIS readings, and closed-loop fluid and vasopressor systems are being studied. The commercial Sedasys system, approved in the US in 2013 for propofol sedation during endoscopy, was withdrawn by its maker in 2016 after weak sales, a reminder that regulatory approval does not guarantee adoption. AI also assists regional anesthesia. Ultrasound software such as ScanNav highlights nerves and surrounding structures on the image to help clinicians identify anatomy. The main misconception is that these systems run anesthesia by themselves. They are decision support or narrow controllers, and a qualified clinician stays responsible and must be ready to override them at any moment.