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Review
. 2024 Nov 10;16(11):e73383.
doi: 10.7759/cureus.73383. eCollection 2024 Nov.

Harnessing Artificial Intelligence (AI) in Anaesthesiology: Enhancing Patient Outcomes and Clinical Efficiency

Arnesh Shukla  1 , Ayesha Salma  2 , Dev Patel  3 , Jabez David John  4 , Reshmitha Kantamneni  5 , Tirath Patel  6 , Ketan Kantamaneni  7
Affiliations
Review

Harnessing Artificial Intelligence (AI) in Anaesthesiology: Enhancing Patient Outcomes and Clinical Efficiency

Arnesh Shukla et al. Cureus. .

Abstract

The rapid rise and potential of artificial intelligence (AI) have created growing excitement and much debate on its potential to bring transformative changes across entire industries, including the medical industry. This systematic review aims to investigate the advancements in the AI industry and its potential implementation, specifically in the field of anaesthesiology. AI has already been integrated into different areas of medicine, including diagnostic uses in radiology and pathology and therapeutic and interventional uses in cardiology and surgery. In the field of anaesthesiology, AI has made significant progress. Potential applications include personalised drug dosing, real-time monitoring of vital signs, automated anaesthesia delivery systems, and predictive analytics for adverse events. As AI technologies continue to advance and become more prevalent in medicine, clinicians across all specialities need to understand these technologies and how they can be utilised to provide safer and more efficient care. With the rapid evolution of AI and the introduction of new concepts such as machine learning (ML), deep learning (DL), and neural networks, the field of anaesthesiology is set to undergo transformative changes. In this systematic review, we examine the existing literature to explore the current state of AI in the field of anaesthesiology, along with a prospective look at potential applications in the future. Along with its various applications, we will also discuss its limitations and flaws. As the field progresses, it is crucial to thoughtfully examine the ethical aspects of using AI in anaesthesia and ensure these technologies are applied responsibly and transparently.

Keywords: ai; anesthesiology; artificial intelligence (ai); clinical efficiency; patient outcomes.

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Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Various applications that will be discussed in this review.
OR: operating room Image credits: Arnesh Shukla. Created in Biorender.com [9]
Figure 2
Figure 2. PRISMA flowchart.
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses [23]
Figure 3
Figure 3. The machine learning (ML) categories and some use-case scenarios for each.
Image credits: Arnesh Shukla. Created in Biorender.com [9]
See this image and copyright information in PMC

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