ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1595060
Exploring the Growth and lmpact of Artificial Intelligence in Anesthesiology: A Bibliometric Study from 2004 to 2024
Provisionally accepted
- Department of Anesthesiology, Shenzhen Second People's Hospital, Shenzhen, China
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The integration of artificial intelligence (AI) in anesthesiology is revolutionizing clinical practice by enhancing patient monitoring, improving risk assessment, and enabling personalized anesthetic care. This bibliometric analysis aims to evaluate publication trends, key contributors, and emerging translational pathways in AI research in anesthesiology, with special emphasis on clinical relevance, thematic clustering, and future application prospects. Materials and Methods: Publications related to AI in anesthesiology from 2004 to 2024 were retrieved from the Web of Science Core Collection database, resulting in 658 articles. VOSviewer and CiteSpace were employed for the bibliometric analysis. Results: AI research in anesthesiology has experienced substantial growth, with a notable surge between 2019 and 2020. The United States leads in both publication volume and citation impact, reflecting its central role in advancing AI-driven innovations. Major journals such as Anesthesia and Analgesia and Anesthesiology play central roles in disseminating key findings. Keyword and journal cluster analyses revealed three major translational domains: real-time perioperative risk prediction (e.g., hypotension, mortality), AI-assisted ultrasound for regional anesthesia, and intelligent anesthesia monitoring systems. Despite progress, emerging concerns such as model interpretability, patientcentered outcomes, and multimodal data integration remain underexplored. Conclusions: AI in anesthesiology is entering a phase of rapid interdisciplinary expansion, integrating clinical needs with computational innovation. Future research should prioritize the clinical validation of AI tools, foster stronger collaboration between computer scientists and anesthesiologists, and address unresolved translational gaps such as model interpretability and cross-modal data fusion.
Keywords: intraoperative monitoring, Predictive Modeling, VOSviewer, Citespace, bibliometric analysis
Received: 17 Mar 2025; Accepted: 16 May 2025.
Copyright: © 2025 Liu, Qiu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xinping Yang, Department of Anesthesiology, Shenzhen Second People's Hospital, Shenzhen, China
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