Journal
BMC Anesthesiology
BMC Anesthesiology is an open access journal publishing original peer-reviewed research articles in all aspects of anesthesiology, critical care, perioperative care and pain...
BMC Anesthesiology is calling for submissions to our collection, Artificial intelligence in anesthesiology.
The integration of artificial intelligence (AI) in perioperative medicine represents a transformative leap forward in anesthesiology. AI technologies, particularly machine learning algorithms, are being used to enhance patient monitoring, optimize anesthesia delivery, and improve decision-making processes throughout the perioperative continuum. By leveraging vast amounts of data, AI can assist clinicians in identifying risk factors, predicting complications, and personalizing anesthetic approaches to individual patients, thereby enhancing overall patient outcomes.
Using AI in anesthesiology has the potential to improve safety, efficiency, and precision in patient care. Recent advancements have demonstrated how AI-supported tools can streamline preanesthetic evaluations, facilitate intraoperative monitoring, and aid in postoperative recovery strategies. These innovations not only enhance the capabilities of anesthesia providers but also hold the promise of reducing the cognitive burden on clinicians, allowing them to focus more on patient-centered care. As the field continues to evolve, further exploration of AI applications will be crucial for improving anesthesiology practices.
The collection aims to explore how AI can transform anesthesiology by improving preanesthetic evaluations through precise risk prediction and personalized planning. We hope to advance intraoperative monitoring with tools that enhance real-time precision and safety, while supporting clinicians with smarter decision-making capabilities. For postoperative recovery, the goal is to leverage AI to optimize patient outcomes and streamline care processes. Ultimately, we seek innovative submissions that push the boundaries of safety, efficiency, and patient-centered care across the perioperative continuum.
We invite submissions from all aspects of this field, including, but not limited to:
•AI-driven insights for preanesthetic evaluation and risk assessment, incorporating human-computer interaction to refine clinician workflows and ensure robust safety frameworks
•Machine learning advancements for real-time intraoperative monitoring, leveraging methodological breakthroughs like multi-agent systems and AI monitoring protocols to enhance patient safety
•AI-enhanced tools for decision-making in anesthesia care, blending human oversight with computational precision while addressing evaluation benchmarks for performance and reliability
•Cutting-edge AI innovations to optimize postoperative recovery and outcomes, utilizing advanced methodologies such as real-time predictive analytics and human-in-the-loop computing
•Development and validation of monitoring and safety frameworks for AI systems in anesthesiology, ensuring resilience against errors and adaptability to clinical variability
• •Evaluation benchmarks and standardized metrics to assess AI tools’ effectiveness, fairness, and generalizability across diverse patient populations
•Regulatory and legal implications of AI deployment in anesthesia, exploring compliance with healthcare standards, ethical considerations, and liability in clinical practice
•Potential multimodal data processing (including real-life intraoperative images or sounds) to design novel AI-driven decision support systems.
As research in this area continues to grow, we can anticipate groundbreaking developments such as real-time predictive analytics for anesthesia-related complications, AI-driven individualized anesthetic plans based on genetic data, and automated systems capable of adjusting anesthesia delivery in response to intraoperative changes. These advancements could revolutionize the way anesthesia is practiced, ultimately leading to better patient outcomes and a more efficient healthcare system.
All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.Submit your manuscript to this collection through the participating journal.
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Maxime P. Cannesson, MD, PhD
University of California Los Angeles, USA
Professor Maxime P. Cannesson is Chair of the Department of Anesthesiology at UCLA. He has been studying artificial intelligence in healthcare since 2005, and has published more than 250 peer reviewed manuscripts. -
Rishi Kamaleswaran, PhD
Duke University School of Medicine, USA
Dr Rishi Kamaleswaran, PhD, is an Associate Professor of Surgery and Anesthesiology at Duke University School of Medicine. A computer scientist by training, he develops machine learning models using multimodal data to improve patient outcomes, with expertise spanning ICU physiology, omics data, and biomedical engineering. Much of his recent work involves modeling complex multimodal insight to study the mechanisms behind the onset of deterioration in critically ill and immunocompromised patients across the lifespan, such as progression to single or multiple organ dysfunction, sepsis, respiratory and neurological dysfunction. -
Joo Heung Yoon, MD
University of Pittsburgh, USA
Dr Joo Heung Yoon is a physician scientist devoted to the development and implementation of physiologic machine learning (ML) models to the critically ill patients to predict hemodynamic instability. With the support of the National Institutes of Health, he has been working with various ML models to identify upcoming risks, along with reinforcement learning and federated learning models to design management strategies. Recently he studied the feasibility of clinical foundation models and associated large language models with objective evaluation metrics, which could directly influence the critical care environment.
