Artificial intelligence is increasingly used to support anesthesiologists' decision-making, though usability, workflow integration, trust, de-skilling, and accountability must be addressed.
This review highlights the growing role of artificial intelligence in supporting anesthesiologists' decision-making while emphasizing the need to address workflow integration, trust, and accountability.
PURPOSE OF REVIEW: This article explores the impact of recent applications of artificial intelligence on clinical anesthesiologists' decision-making. RECENT FINDINGS: Naturalistic decision-making, a rich research field that aims to understand how cognitive work is accomplished in complex environments, provides insight into anesthesiologists' decision processes. Due to the complexity of clinical work and limits of human decision-making (e.g. fatigue, distraction, and cognitive biases), attention on the role of artificial intelligence to support anesthesiologists' decision-making has grown. Artificial intelligence, a computer's ability to perform human-like cognitive functions, is increasingly used in anesthesiology. Examples include aiding in the prediction of intraoperative hypotension and postoperative complications, as well as enhancing structure localization for regional and neuraxial anesthesia through artificial intelligence integration with ultrasound. SUMMARY: To fully realize the benefits of artificial intelligence in anesthesiology, several important considerations must be addressed, including its usability and workflow integration, appropriate level of trust placed on artificial intelligence, its impact on decision-making, the potential de-skilling of practitioners, and issues of accountability. Further research is needed to enhance anesthesiologists' clinical decision-making in collaboration with artificial intelligence.
Duran et al. (Fri,) conducted a review in Anesthesiology decision-making. Artificial intelligence was evaluated. Artificial intelligence is increasingly used to support anesthesiologists' decision-making, though usability, workflow integration, trust, de-skilling, and accountability must be addressed.