- Define core artificial intelligence terminology and describe how clinical AI systems are trained, validated, and implemented in perioperative care.
- Interpret AI model outputs using performance metrics and identify potential bias, limitations, and errors.
- Apply a structured approach to critically appraise AI recommendations before incorporating them into clinical workflow.
This program has been prior approved by the American Association of Nurse Anesthesiology for 1.00 MAC Ed CE credits; Code Number 1047215; Expiration Date 8/31/2029.
Module 2: Ethical Foundations of Clinical AI
As artificial intelligence becomes increasingly integrated into perioperative care, clinicians must be prepared to apply established ethical principles to new questions surrounding AI-assisted decision-making.
Ethical Foundations of Clinical AI explores how principles such as autonomy, beneficence, nonmaleficence, justice, transparency, privacy, and professional accountability apply when clinicians use AI-supported technologies. Learners examine issues including algorithmic bias, explainability, patient impact, informed decision-making, human oversight, automation dependence, and institutional governance.
Through realistic scenarios and case-based activities, learners practice identifying ethical risks and evaluating AI technologies while maintaining patient safety, clinical judgment, and professional responsibility.
Learning Outcomes:
- Define key ethical principles relevant to AI use in healthcare.
- Describe how algorithmic bias and data limitations influence clinical AI performance.
- Apply ethical frameworks to evaluate AI-supported clinical decision-making scenarios in perioperative care.
- Recognize situations in which disclosure of AI involvement may be ethically appropriate.
- Analyze perioperative case examples to identify risks related to bias, privacy, and automation dependence.
- Examine how institutional policies influence ethical AI deployment.
- Develop a structured ethical review framework for evaluating AI technologies prior to clinical implementation.
This program has been prior approved by the American Association of Nurse Anesthesiology for 1.00 MAC Ed CE credits; Code Number 1047214; Expiration Date 8/31/2029.
Module 3: Artificial Intelligence in Anesthesia: Current Applications and Near-Future Innovations in Perioperative Practice
Artificial intelligence is moving from a theoretical concept to an increasingly relevant component of perioperative care. Artificial Intelligence in Anesthesia: Current Applications and Near-Future Innovations in Perioperative Practice connects foundational AI knowledge to anesthesia-specific clinical applications.
Learners explore how AI is being applied across the perioperative continuum, including preoperative risk stratification, predictive analytics, physiologic monitoring, clinical decision support, AI-assisted imaging, documentation, workflow optimization, and emerging closed-loop anesthesia systems.
The course emphasizes the CRNA's role in interpreting AI-generated information rather than simply accepting automated recommendations. Case-based activities challenge learners to evaluate the safety, validity, clinical usefulness, and limitations of AI-supported technologies while maintaining clinician oversight and accountability.
Learning Outcomes:
- Identify current and emerging applications of AI in anesthesiology and perioperative medicine.
- Recognize appropriate and inappropriate use cases for generative AI tools in clinical and educational settings.
- Integrate AI-derived insights into perioperative decision-making while maintaining clinical accountability.
- Utilize structured frameworks to evaluate the safety and appropriateness of AI-enabled clinical tools.
- Analyze real-world perioperative case scenarios involving AI-supported risk prediction or decision support.
- Examine institutional, regulatory, and ethical factors influencing AI implementation in healthcare systems.
- Assess medicolegal and professional responsibility considerations associated with AI-assisted care.
- Differentiate between AI-generated recommendations and clinician-derived decision-making in perioperative scenarios.
- Describe key AI technologies (machine learning, deep learning, predictive analytics) relevant to anesthesiology practice.
- Identify current clinical applications of AI across the perioperative continuum, including risk prediction, physiologic monitoring, and clinical decision support.
- Explain the principles and potential benefits of closed-loop anesthesia systems and automated drug delivery technologies.
- Evaluate emerging AI tools for airway assessment, ultrasound-guided procedures, and postoperative complication prediction.
- Discuss ethical, regulatory, and implementation challenges associated with integrating AI into anesthesia practice.
- Assess the future role of anesthesia providers in a healthcare environment increasingly augmented by intelligent system.
This program has been prior approved by the American Association of Nurse Anesthesiology for 2.00 MAC Ed CE credits; Code Number 1047203; Expiration Date 8/31/2029.