AI co-pilot helps anesthesiologists keep children safer in surgery

AI is emerging as a co-pilot in pediatric anesthesia, offering earlier hypoxemia alerts and more accurate pain scoring. It also improves breathing tube choices and placement.

AI Is Emerging as a Patient Safety Tool in Pediatric Anesthesia

Artificial intelligence is moving from concept to clinical utility in the operating room. A systematic review presented at the ANESTHESIOLOGY 2025 annual meeting suggests AI can improve safety and recovery for children by supporting anesthesiologists with real-time insights and more precise decisions.

Pediatric anesthesia is hard to standardize. Kids of the same age can have very different airway anatomy, physiology and pain responses. The review found AI models outperformed standard methods for selecting and placing breathing tubes, anticipating drops in oxygen saturation and assessing postoperative pain.

What the Evidence Shows

Earlier hypoxemia warnings: Trained on data from more than 13,000 surgeries, AI systems analyzed second-by-second signals from anesthesia machines. The most efficient model flagged risk up to 60 seconds before standard alarms, giving clinicians critical time to adjust ventilation, clear secretions or correct the airway.
More accurate pain assessment: Using over 1,000 observations from 149 toddlers (crying, agitation, guarding, facial expressions), an AI tool reached 95% accuracy, compared with 85%-88% for common scales such as FLACC and Wong-Baker faces.
Better breathing tube selection and placement: In 37,000 children, machine-learning models predicted endotracheal tube size and depth more accurately than age/height formulas, cutting errors by 40%-50%.

"Think of AI as the co-pilot, while the anesthesiologist makes all the final decisions. It can spot subtle changes sooner and tailor decisions to each child's unique anatomy, adding another layer of safety and support," said a lead author of the study.

Why This Matters for Clinical Leaders and Researchers

Safety margin: A one-minute early warning on oxygen saturation can be the difference between a quick correction and a serious event.
Precision over averages: Moving beyond age-based formulas reduces airway injury risk and improves ventilation quality.
Objective pain data: More consistent pain scoring supports better dosing and faster recovery.
Operational consistency: Decision support that reduces variability can standardize quality across teams and shifts.

Readiness Checklist Before Deployment

External validation: Confirm performance across diverse ages, weights, comorbidities, ethnicities and device vendors.
Integration: Seamless connection with anesthesia machines, monitors and the anesthesia information management system (AIMS).
Human factors: Clear, actionable alerts that minimize alarm fatigue and fit into OR workflows.
Governance: Define clinical guardrails, escalation paths and documentation standards for AI-assisted decisions.
Bias and reliability: Ongoing drift monitoring, audit trails and periodic revalidation.
Privacy and security: Strong controls for perioperative data streams; align with institutional and regulatory requirements for software as a medical device.

How to Pilot This Responsibly

Start with a safety KPI bundle: Rate and duration of hypoxemia, unplanned airway interventions, reintubation, PACU pain scores, opioid use and postoperative adverse events.
Prospective, clinician-in-the-loop trials: Silent mode first (alerts recorded but not shown), then supervised mode with explicit override and feedback logging.
Training and adoption: Short scenario-based drills for anesthesiologists and OR nurses; clear "what to do when this alert fires" playbooks.
Procurement questions: Data sources, model update cadence, on-device vs. cloud processing, uptime SLAs, cybersecurity posture and support for local calibration.

"For parents, the real value of AI is peace of mind," said a co-author of the study. For leaders, the value is a measurable reduction in preventable harm with tools that augment, not replace, expert judgment.