The Current Use of Artificial Intelligence in Anesthesiology
Clinical Snapshot
PICO Framework
| P — Population | Anesthesiology practice and patient care |
| I — Intervention | Artificial intelligence applications (machine learning, deep learning, natural language processing) |
| C — Comparator | Current standard anesthesiology practice without AI |
| O — Outcomes | Patient monitoring enhancement, drug administration optimization, decision-making improvement, postoperative complication prediction |
Bottom Line
This narrative review examines the current limited use of artificial intelligence in anesthesiology, highlighting potential applications in patient monitoring, drug optimization, clinical decision-making, and complication prediction. While the technology shows promise for enhancing perioperative care through machine learning and natural language processing, widespread adoption remains limited. The review identifies key areas where AI could transform anesthesiology practice, but lacks specific evidence of clinical effectiveness or implementation challenges. For Australian practitioners, any AI applications would require TGA approval and careful integration with existing perioperative workflows. The field appears to be in early stages compared to other medical specialties like radiology, suggesting opportunities for future development and research.
Key Findings
P Value: Not applicable for narrative review
Effect Size: Not quantified in abstract
Primary Outcome: Current state of AI adoption in anesthesiology is limited despite potential benefits
Nnt Or Sensitivity: Not applicable - descriptive review of current applications
Confidence Interval: Not provided
Clinical Application
Implementation would require significant technological infrastructure and training Relevant to Australian anesthesiology practice; would need consideration of TGA approval for medical devices and integration with existing hospital systems Anesthesiologists and perioperative care teams globally
Abstract
Artificial intelligence (AI) applications are increasingly being used in areas of medicine such as radiology. However, within anesthesiology, there is not widespread use of AI applications at this time, though it has the potential to have a profound impact. Many applications of AI, including machine learning, deep learning, and natural language processing, are being used to create applications to enhance patient monitoring, optimize drug administration, improve decision-making, and predict postoperative complications.
References
- 1.Feldheim, T. V., Brennan, M., & Tighe, P. (2025). The current use of artificial intelligence in anesthesiology. Advances in Anesthesia, 43, [page numbers]. https://doi.org/10.1016/j.aan.2025.07.006
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