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. 2024 Aug 1;141(2):379-387.
doi: 10.1097/ALN.0000000000005013.

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Lichy Han  1 , Danton S Char  1 , Nima Aghaeepour  1 ; Stanford Anesthesia AI Working Group
Collaborators, Affiliations

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Lichy Han et al. Anesthesiology. .

Abstract

Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.

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Conflict of interest statement

Conflicts of Interest: ERG is a consultant for Chiima therapeutics, receives research funding from the National Institutes of Health and the University of California Tobacco Related Disease Research Program, and is an Associate Editor for JPET editorial board. MWV receives funding from the Dana-Farber Cancer Institute/Novartis for patent royalties on novel cancer immunotherapy. N.A. is a cofounder of Takeoff AI, a member of the Scientific Advisory Boards of January AI, Parallel Bio, Celine Therapeutics, and WellSim Biomedical Technologies and is a paid consultant for MaraBio Systems.

The remaining authors declare no competing interests.

Figures

Figure 1.
Figure 1.
By integrating vast quantities of clinical observation, monitoring, diagnostic, and patient reported data, AI models have the potential to enhance numerous domains within anesthesia practice.
Figure 2.
Figure 2.
Life cycle of AI model development and maintenance. Data need to be collected, cleaned, processed, and validated prior to model construction. Models constructed then undergo validation prior to implementation, with constant surveillance and generation of new data to update and improve existing models.
See this image and copyright information in PMC

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