AI Research in Anesthesiology Grew From a Niche Interest to a Substantial Field Over Twenty Years, With a Sharp Surge After 2019. The Three Areas Driving That Growth Are Perioperative Risk Prediction, AI-Assisted Ultrasound Guidance, and Intelligent Monitoring.

Anesthesiology is unusually data-rich. Every surgical patient generates continuous physiological data across a compressed time window: blood pressure, heart rate, oxygen saturation, depth of anesthesia, neuromuscular blockade, temperature. Small deviations from optimal management can carry significant consequences in that window, and clinical decision-making inside it is rapid, often intuitive, and heavily dependent on individual provider experience. Those two features, abundant real-time data and time-pressured expert judgment, make anesthesiology a natural environment for AI augmentation. This bibliometric analysis from researchers at Shenzhen Second People's Hospital maps the full trajectory of AI research in anesthesiology from 2004 to 2024, identifies where the field is most active, and characterizes the three translational domains where clinical integration is furthest along. My exposure to the clinical environment of anesthesia and longstanding interest in perioperative medicine is what drew me to this paper, and it informs the perspective behind this review.

Liu, Qiu, and Yang retrieved all publications related to AI in anesthesiology from the Web of Science Core Collection covering January 2004 through December 2024, yielding 658 articles. Bibliometric methods using VOSviewer and CiteSpace software were applied to map publication trends, identify the most prolific contributing countries and institutions, characterize the most influential journals, and conduct keyword co-occurrence and cluster analysis to identify the dominant research themes. The analysis focused on clinical relevance and translational pathways rather than publication volume alone.

AI research in anesthesiology grew substantially across the study period, with a notable surge between 2019 and 2020 corresponding to the maturation of deep learning and the rapid expansion of large clinical datasets. The United States leads in both publication volume and citation impact. Anesthesia and Analgesia and Anesthesiology are the primary dissemination venues for high-impact work. Keyword and cluster analyses identified three major translational domains. The first is real-time perioperative risk prediction, with hypotension prediction and intraoperative mortality risk as the most active areas. The second is AI-assisted ultrasound for regional anesthesia, where models are being developed to automate nerve identification, optimize needle trajectory guidance, and reduce the learning curve for ultrasound-guided nerve blocks. The third is intelligent anesthesia monitoring, encompassing AI applications in depth of anesthesia assessment, automated ventilator management, and closed-loop drug delivery. The authors flag persistent gaps across all three: model interpretability, patient-centered outcome measurement, and multimodal data integration remain underexplored relative to their importance for clinical translation.

• —Bibliometric analyses map published research, not clinical deployment. A growing body of AI anesthesiology literature does not mean those systems are being used in operating rooms. The gap between research output and actual integration is substantial and not captured here.
• —The Web of Science Core Collection may not index all relevant publications, particularly conference proceedings and preprints, which are significant dissemination channels in the AI field.
• —Keyword co-occurrence analysis identifies dominant themes in aggregate but cannot assess the quality, reproducibility, or clinical validity of individual studies within each cluster.
• —The study covers publications through December 2024 and does not capture the most recent period of a rapidly evolving field.

AI is entering anesthesiology through the same path it has taken in every data-rich clinical specialty, starting where the data is richest and the clinical problem is most amenable to pattern recognition. Perioperative hypotension prediction, ultrasound guidance, and intelligent monitoring are the leading edge. The questions that will determine how fast this integration proceeds are not primarily technical. They are about interpretability, accountability, validation standards, and what role automated decision support should play when the patient is sedated and cannot speak for themselves.