Featured Articles: Original Clinical Research Report
Estrada Alamo, Carlos E. MD, MBA*; Diatta, Fortunay MD, MBE†; Monsell, Sarah E. MS‡; Lane-Fall, Meghan B. MD, MSHP, FCCM§
From the *Department of Anesthesiology, Virginia Mason Medical Center, Seattle, Washington
†Division of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut
‡Department of Biostatistics, University of Washington, Hans Rosling Center for Population Health, Seattle, Washington
§Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Accepted for publication August 31, 2023.
Reprints will not be available from the authors.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: C.E.E.A. was supported by an American Society of Anesthesiologists Mentoring Grant. The views expressed are those of the author(s) and not necessarily those of the funder, Yale Medicine, Penn Medicine, Virginia Mason Medical Center, or the Departments of Anesthesiology at these institutions.
The authors declare no conflicts of interest.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website.
Previously presented at the Anesthesiology 2021 Annual Meeting | Barriers to Adopting Artificial Intelligence in Anesthesiology, poster presentation; October 8–12, 2021; San Diego, CA (virtual presentation); and the Society for Technology in Anesthesia 2022 Annual Meeting | Barriers to Adopting Artificial Intelligence in Anesthesiology, poster presentation; January 12–15, 2022; Four Seasons Hotel in Las Vegas, Nevada (virtual presentation).
This study was deemed exempt research by the University of Pennsylvania Institutional Review Board on November 23, 2020 (protocol no. 844568).
The authors declare that data supporting the survey findings are available within the paper and the supplementary files. Raw data are available on request.
During the preparation of this manuscript, the authors utilized the ChatGPT Default model (version GPT-3.5) developed by OpenAI as an AI-based tool to enhance clarity, coherence, and overall quality, while maintaining the original creative and intellectual contributions of the authors. After utilizing this tool, the authors reviewed and edited the content as necessary, taking full responsibility for the publication’s content.
Address correspondence to Carlos E. Estrada Alamo, MD, MBA, Department of Anesthesiology, Virginia Mason Medical Center, 1100 9th Ave, Seattle, WA 98101. Address e-mail to [emailprotected]
Anesthesia & Analgesia 138(5):p 938-950, May 2024. | DOI: 10.1213/ANE.0000000000006752
- Buy
- SDC
Abstract
BACKGROUND:
This study explored physician anesthesiologists’ knowledge, exposure, and perceptions of artificial intelligence (AI) and their associations with attitudes and expectations regarding its use in clinical practice. The findings highlight the importance of understanding anesthesiologists’ perspectives for the successful integration of AI into anesthesiology, as AI has the potential to revolutionize the field.
METHODS:
A cross-sectional survey of 27,056 US physician anesthesiologists was conducted to assess their knowledge, perceptions, and expectations regarding the use of AI in clinical practice. The primary outcome measured was attitude toward the use of AI in clinical practice, with scores of 4 or 5 on a 5-point Likert scale indicating positive attitudes. The anticipated impact of AI on various aspects of professional work was measured using a 3-point Likert scale. Logistic regression was used to explore the relationship between participant responses and attitudes toward the use of AI in clinical practice.
RESULTS:
A 2021 survey of 27,056 US physician anesthesiologists received 1086 responses (4% response rate). Most respondents were male (71%), active clinicians (93%) under 45 (34%). A majority of anesthesiologists (61%) had some knowledge of AI and 48% had a positive attitude toward using AI in clinical practice. While most respondents believed that AI can improve health care efficiency (79%), timeliness (75%), and effectiveness (69%), they are concerned that its integration in anesthesiology could lead to a decreased demand for anesthesiologists (45%) and decreased earnings (45%). Within a decade, respondents expected AI would outperform them in predicting adverse perioperative events (83%), formulating pain management plans (67%), and conducting airway exams (45%). The absence of algorithmic transparency (60%), an ambiguous environment regarding malpractice (47%), and the possibility of medical errors (47%) were cited as significant barriers to the use of AI in clinical practice. Respondents indicated that their motivation to use AI in clinical practice stemmed from its potential to enhance patient outcomes (81%), lower health care expenditures (54%), reduce bias (55%), and boost productivity (53%). Variables associated with positive attitudes toward AI use in clinical practice included male gender (odds ratio [OR], 1.7; P < .001), 20+ years of experience (OR, 1.8; P < .01), higher AI knowledge (OR, 2.3; P = .01), and greater AI openness (OR, 10.6; P < .01). Anxiety about future earnings was associated with negative attitudes toward AI use in clinical practice (OR, 0.54; P < .01).
CONCLUSIONS:
Understanding anesthesiologists’ perspectives on AI is essential for the effective integration of AI into anesthesiology, as AI has the potential to revolutionize the field.
