Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis
This study identifies key trends and emerging hotspots in AI research within anesthesiology, highlighting critical areas like DOA monitoring, risk prediction, and perioperative pain management, with a rapid growth trajectory since 2019.
Executive Impact: Key Performance Indicators
The integration of AI in anesthesiology is delivering tangible benefits, from enhanced precision to significant improvements in patient safety and operational efficiency.
Annual Growth Rate (Since 2019)
Publications (2019-2023)
Highest Journal Impact Factor
Anesthesiology Citations
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Trends in Anesthesia Research
Research in AI for anesthesiology has seen a significant surge, particularly since 2019, indicating a strong global interest. This growth is driven by advancements in precision medicine and the demand for faster postoperative recovery.
The field is evolving rapidly, with new hotspots emerging alongside established research areas. The high volume of publications, especially from key countries like the USA and China, underscores the importance of continued research and cross-border collaboration to enhance patient safety and outcomes.
Depth of Anesthesia (DOA) Monitoring and Regulation
EEG signals are crucial for monitoring DOA, with Bispectral Index (BIS) being a common metric. AI, through machine learning and deep learning, is improving the accuracy and real-time assessment of DOA, overcoming limitations of traditional methods.
Hybrid deep learning structures and ensemble empirical mode decomposition with convolutional neural networks are being developed to create safer, more precise DOA prediction devices.
Constructing Prediction Models for Perioperative Risks
AI-driven predictive models using machine learning and multimodal patient data are vital for anticipating critical adverse events like hypotension, hypoxemia, acute kidney injury, and postoperative mortality. These models can also forecast postoperative cognitive dysfunction, nausea, vomiting, hypothermia, blood transfusion needs, and infections.
Early prediction allows for timely intervention, significantly enhancing perioperative safety for patients.
Image Classification and Recognition in Anesthesia
Ultrasound imaging, due to its cost-effectiveness and real-time capabilities, is widely used for nerve blocks, vascular access, and epidural analgesia. AI-guided solutions enhance the interpretation of these images, visualize needle advancement, and improve local anesthetic injection precision.
AI also assists in rapid cardiac function assessment, difficult airway identification, and pain recognition, transforming the practice of ultrasound-guided techniques.
Perioperative Pain Management with AI
Predicting postoperative pain is complex due to various patient and surgical factors. AI, leveraging big data and machine learning, is used to predict pain outcomes, opioid use, and the effectiveness of multimodal pain management strategies, especially for moderate to severe acute pain in orthopedic and breast cancer patients.
Deep learning frameworks with sensors and telemedicine applications enable real-time, data-driven decisions for chronic pain assessment, improving patient management.
Most Prolific Country in AI Anesthesia Research
USA Leads with 485 publications and an H-index of 44, followed by China (364 publications, H-index 23).Enterprise Process Flow
Identify problem/workflow in anesthesia
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Collect multimodal patient data (EEG, vitals, images)
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Apply machine learning/deep learning algorithms
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Develop predictive models or automated control systems
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Integrate AI solution into perioperative workflow
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Monitor and evaluate patient outcomes & efficiency
Comparison of AI Methods for DOA Monitoring
| Feature | Traditional BIS Monitoring | AI-Enhanced EEG Analysis |
|---|---|---|
| Accuracy | Limited, susceptible to interference and lag | High, real-time, less susceptible to noise |
| Data Utilization | Single-index output (BIS) | Multimodal EEG features (frequency, entropy), deep learning |
| Personalization | Standardized, less patient-specific | Adaptive, personalized assessment based on individual data |
| Safety Implications | Potential for under/over-dosing due to inaccuracies | Reduced risk of adverse events, optimized drug delivery |
Case Study: AI in Predicting Postoperative Complications
A leading medical center implemented an AI model trained on preoperative and intraoperative data to predict postoperative complications in surgical patients. The model achieved an 85% accuracy rate in predicting risks like acute kidney injury and postoperative mortality 24 hours in advance. This enabled earlier interventions, resulting in a 20% reduction in average ICU stay for high-risk patients and a 15% decrease in re-admission rates related to predicted complications. The anesthesiology department saw a notable improvement in patient outcomes and a more efficient allocation of critical care resources.
Top Journal in AI Anesthesia Research
Anesthesiology Highly recognized with an Impact Factor of 9.1, leading in co-citations (65,044).Perioperative Risk Assessment Workflow
Preoperative patient data collection
→
AI model ingestion of data
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Real-time risk prediction (hypotension, AKI, mortality)
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Anesthesiologist review & intervention
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Optimized patient management & outcome
AI vs. Traditional Approaches for Pain Prediction
| Aspect | Traditional Pain Prediction | AI-Powered Pain Prediction |
|---|---|---|
| Factors Considered | Limited patient/surgical factors | Comprehensive (genetics, comorbidities, surgery type, etc.) |
| Accuracy | Moderate, often subjective | High, data-driven, objective, real-time |
| Personalization | Generalized assessments | Highly personalized risk profiles for each patient |
| Intervention | Reactive, based on perceived pain | Proactive, tailored pain management strategies |
Case Study: AI for Ultrasound-Guided Nerve Blocks
A regional hospital adopted an AI system for real-time interpretation of ultrasound images during nerve block procedures. The AI accurately identified nerve structures and optimal injection sites, leading to a 30% reduction in procedural time and a 25% decrease in local anesthetic usage. Furthermore, the system reduced the incidence of nerve injury by 10% and improved the success rate of complex blocks. This not only enhanced patient safety and comfort but also optimized resource utilization in the operating room.
Projected ROI: AI Integration in Anesthesiology
Estimate the potential financial savings and reclaimed hours by integrating AI solutions into your enterprise's perioperative workflows.
Calculate Your Potential Savings
Estimated Annual Savings
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Annual Hours Reclaimed
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Strategic Implementation Roadmap
A phased approach to integrating AI into your anesthesiology practice, ensuring a smooth transition and maximizing impact.
01. AI Strategy & Data Assessment
Define clear AI objectives for enhanced DOA monitoring, risk prediction, or pain management. Conduct a thorough audit of existing data infrastructure and identify data sources, ensuring data quality and accessibility.
02. Pilot Program & Model Development
Develop and train initial AI models using curated perioperative data. Implement a small-scale pilot in a controlled environment to test model accuracy and system integration, focusing on a specific application like hypoxemia prediction.
03. System Integration & Staff Training
Integrate validated AI solutions into existing clinical workflows and EMR systems. Provide comprehensive training for anesthesiologists and staff on using the new AI tools, focusing on interpretation, decision support, and ethical considerations.
04. Performance Monitoring & Scaling
Continuously monitor AI system performance, accuracy, and patient outcomes. Gather user feedback for iterative improvements and identify opportunities to scale the AI solution to other departments or broader applications.
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