ReviewAnesthesiology2019
Artificial Intelligence and Machine Learning in Anesthesiology.
Review in Anesthesiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 86 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pilot Observational Cohort Study to Validate the Aiinane Preoperative and Preanesthesia Risk Assessment Tool in Real-world Conditions: Proof of Concept.
AI-assisted Analgesia Copilot System for Proper Management of Nociception
Who cites it
86 citing papers in PubMed.
- Examining the impact perceptual learning artificial-intelligence-based on the incidence of paresthesia when performing the ultrasound-guided popliteal sciatic block: simulation-based randomized study.BMC anesthesiology · 2022Trial
- A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study.BMC anesthesiology · 2026Observational
- An Emerging Role of Artificial Intelligence in Pediatric Neuroanesthesia.Journal of Korean Neurosurgical Society · 2026Article
- Clinical Application of Artificial Intelligence in Anesthesiology: A Multicenter Retrospective Comparison Between Human Anesthetic Decisions and Algorithmic Recommendations in Non-Cardiac Surgery.Journal of personalized medicine · 2026Article
- Predicting Difficult Tracheal Intubation Using Multi-Angle Photographic Analysis with Convolutional Neural Networks and EfficientNet.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Perioperative machine learning models with SHAP interpretation for predicting adverse outcomes in breast cancer surgery.Frontiers in surgery · 2026Article
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Emerging technologies in airway management: a narrative review of intubation robotics and anatomical structure recognition algorithms.Biomedical engineering online · 2025Review
- Machine Learning-Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Forecasting intraoperative hypotension during hepatobiliary surgery.Journal of clinical monitoring and computing · 2025Article
- Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025Article
- Preventive strategies for reducing intraoperative awareness due to errors in anesthetic drug delivery systems: a narrative review.Frontiers in pharmacology · 2025Review
- Exploring the growth and impact of artificial intelligence in anesthesiology: a bibliometric study from 2004 to 2024.Frontiers in medicine · 2025Article
- Artificial intelligence in anesthesiology: a bibliometric analysis.Perioperative medicine (London, England) · 2024Review
- Data-based regression models for predicting remifentanil pharmacokinetics.Indian journal of anaesthesia · 2024Article
- Harnessing Artificial Intelligence (AI) in Anaesthesiology: Enhancing Patient Outcomes and Clinical Efficiency.Cureus · 2024Review
- Mapping the landscape of machine learning models used for predicting transfusions in surgical procedures: a scoping review.BMC medical informatics and decision making · 2024Article
- BART, the new robotic assistant: big data, artificial intelligence, robotics, and telemedicine integration for an ICU 4.0.Journal of anesthesia, analgesia and critical care · 2024Article
- Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.Scientific reports · 2024Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.