Artificial intelligence in anesthesiology offers significant promise for improving patient safety and workflow efficiency, but requires clinician oversight and ethical safeguards to mitigate risks.
Artificial intelligence has the potential to transform anesthetic care by improving patient safety and workflow efficiency, provided that risks like algorithmic bias and automation complacency are carefully managed.
PURPOSE OF REVIEW: The rapid growth and integration of artificial intelligence in healthcare has the potential to revolutionize all fields of medicine, including anesthesiology. This review summarizes the role of artificial intelligence in anesthesiology, highlighting how it can enhance patient safety, workflow efficiency, and research capabilities, while also examining the potential risks and ethical issues of introducing this new technology. RECENT FINDINGS: Evolving applications in anesthesiology include closed-loop anesthetic delivery, risk stratification, intraoperative monitoring, natural language processing for literature synthesis, and predictive modeling for adverse events. While these tools offer significant promise for improving efficiency and safety, emerging risks, such as algorithmic bias, lack of transparency, hallucinations, and automation complacency - must be carefully addressed. Regulatory frameworks, clinician education, and transparent model development are necessary to ensure responsible integration. SUMMARY: Artificial intelligence is poised to transform anesthetic care, but it must be developed and deployed with caution. Clinician oversight, robust validation, and ethical safeguards are essential to ensure artificial intelligence enhances, rather than replaces, clinical judgment. Strategic adoption can improve patient outcomes, reduce preventable harm, and streamline perioperative workflows.
Maloney et al. (Tue,) conducted a review in Anesthesia patient safety. Artificial intelligence was evaluated. Artificial intelligence in anesthesiology offers significant promise for improving patient safety and workflow efficiency, but requires clinician oversight and ethical safeguards to mitigate risks.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: