Understanding AI in anesthesia and perioperative care.
AI is easy to over- or under-trust. These short guides explain what it can and cannot do, written for the person reading them—whether patient, clinician, researcher, or partner.
Patients & families
Straightforward answers about how AI might appear in anesthesia and perioperative care, and where your care team always stays responsible.
What is AI in healthcare?
AI is software that finds patterns in large amounts of information and uses them to make predictions or generate language. In healthcare it is being studied as a way to support education and communication and some clinical tasks—always alongside, not instead of, your care team.
How might AI be used before, during, or after surgery?
Before surgery it might help explain preparation instructions or help you organize questions. During and after, researchers in the field are exploring support for monitoring and communication. These are examples of research directions, not services offered by CAPITAL or care you receive today.
Will AI replace my anesthesiologist?
No. Research described here is intended to support clinicians, not replace them. Your anesthesiologist and care team remain responsible for decisions about your care.
What questions should I ask my care team?
You can ask what a tool does, what information it uses, whether a person reviews its output, and who to contact with concerns. There is no wrong question.
How is my health information protected?
Research involving personal health information must follow applicable hospital privacy requirements and research-ethics review. Do not submit personal health information through this website; use only the approved channels provided by your care team.
What are the risks of AI?
AI can be wrong, can reflect biases in the data it learned from, and can sound confident while being mistaken. Safety, oversight, and equity therefore need to be studied before any tool is considered for use in care.
How can patients help shape safer AI?
Patient partners can help choose what to study, review whether language is clear, identify risks, and judge whether an outcome actually matters. Lived experience is valuable expertise; no technical background is needed.
Clinicians & trainees
What to understand before trusting, teaching, or using an AI tool in clinical practice.
What clinicians need to know about AI
Enough to appraise it: what a model was trained on, what it was validated against, where it fails, and how it fits the workflow and your accountability.
How to read an AI paper
Look past headline accuracy. Check the population, the reference standard, whether validation was external, and whether the outcome is clinically meaningful rather than only statistically significant.
What is an LLM, and why do hallucinations matter?
A large language model predicts likely text; it does not know facts. It can produce fluent, confident statements that are wrong—called hallucinations—which is why clinical use needs verification and human oversight.
Bias, equity, and generalizability
A model reflects its training data. Performance can differ across populations and settings, so a tool that works in one hospital may not transfer without re-evaluation.
Clinical validation versus model performance
Strong benchmark numbers are not the same as clinical benefit. Validation asks whether the tool improves care in a real setting, with real workflows and real patients.
Workflow integration
A tool that adds clicks, interrupts handoffs, or creates alert fatigue can fail even when it is accurate. Fit with existing work is part of whether it is safe and useful.
Documentation, communication, and medico-legal caution
Be transparent about when AI informed a decision, keep the human judgement documented, and remember that accountability stays with the clinician.
Prompting as a clinical skill—but not a substitute for judgement
Learning to query these tools well is useful, but a good prompt does not make the output true. Prompting supports judgement; it does not replace it.
Researchers
Designing and evaluating clinical AI research that generates trustworthy, usable evidence.
Study design for clinical AI
Match the design to the claim. Usability, feasibility, and comparative-effectiveness questions each need different methods, and a prospective, externally validated design is stronger than a retrospective one.
Usability testing
Observe real users doing real tasks. Where they hesitate, misread, or work around the tool is data about safety, not just polish.
Evaluation metrics
Choose measures that map to the clinical question. Calibration, decision impact, and patient-relevant outcomes often matter more than accuracy alone.
Human factors
How people perceive, trust, and act on a tool shapes its effect. Over-trust and under-trust are both failure modes.
Implementation science
Adoption depends on context: workflow, incentives, training, and governance. Studying implementation is as important as studying the model.
Qualitative methods and patient partnership
Interviews, observation, and co-design surface risks and meanings that metrics miss, and patient partners help define what counts as a useful outcome.
Dataset limitations
Know your data’s provenance, gaps, and biases. Missing or unrepresentative data limits what any result can claim.
Responsible dissemination
Report limitations plainly, avoid overstating readiness, and be careful that public claims do not outrun the evidence.
Hospitals & partners
Questions to ask before piloting, procuring, or scaling an AI tool in a real health system.
What evidence is needed before adopting an AI tool?
At minimum: validation in a population like yours, a clear clinical use case, evidence of workflow fit, and a plan for monitoring after deployment.
How to evaluate clinical fit
Ask where the tool sits in the care pathway, who acts on its output, and what happens when it is wrong or unavailable.
How to assess workflow risk
Map the added steps, the failure modes, the escalation path, and the effect on clinician workload and alert burden.
What governance questions should be asked?
Who is accountable, how is performance monitored over time, how are updates managed, and how is model drift detected?
Privacy, procurement, and accountability
Clarify data flows, storage, vendor obligations, and where liability sits before a pilot begins—not after.
Pilot design checklist
Define the population, the success measures, the safeguards, the oversight, the stop criteria, and the evaluation plan before going live.