Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey.



Cobianchi, Lorenzo, Piccolo, Daniele, Dal Mas, Francesca, Agnoletti, Vanni, Ansaloni, Luca, Balch, Jeremy, Biffl, Walter, Butturini, Giovanni, Catena, Fausto, Coccolini, Federico
et al (show 14 more authors) (2023) Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey. World journal of emergency surgery : WJES, 18 (1). p. 1.

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Abstract

<h4>Background</h4>Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes.<h4>Methods</h4>An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile.<h4>Results</h4>650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust.<h4>Discussion</h4>The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.

Item Type: Article
Uncontrolled Keywords: Team Dynamics Study Group
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences > School of Medicine
Depositing User: Symplectic Admin
Date Deposited: 06 Mar 2024 11:18
Last Modified: 06 Mar 2024 11:18
DOI: 10.1186/s13017-022-00467-3
Open Access URL: https://doi.org/10.1186/s13017-022-00467-3
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3179194