Strengths-weaknesses-opportunities-threats Analysis of Artificial Intelligence in Anesthesiology and Perioperative Medicine

Overview

Journal Front Digit Health

Specialty Medical Informatics

Date 2024 Mar 6

PMID 38444721

Authors

Henry J Paiste

Ryan C Godwin

Andrew D Smith

Dan E Berkowitz

Ryan L Melvin

Affiliations

Soon will be listed here.

Abstract

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

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