ArticleTurkish journal of anaesthesiology and reanimation2025
Artificial Intelligence in Anaesthesiology: Current Applications, Challenges, and Future Directions.
Article in Turkish journal of anaesthesiology and reanimation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence for Perioperative Risk Prediction in Anesthesiology: A Bibliometric and Knowledge-Mapping Analysis.Healthcare (Basel, Switzerland) · 2026Review
- From Publication to Scientific Communication: The Expanding Role of Social Media in the Future of TJAR.Turkish journal of anaesthesiology and reanimation · 2026Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 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
- Response to "Reflection on the Integration of Artificial Intelligence in Anaesthesiology: Beyond Algorithmic Performance".Turkish journal of anaesthesiology and reanimation · 2026Article
- Reflection on the Integration of Artificial Intelligence in Anaesthesiology: Beyond Algorithmic Performance.Turkish journal of anaesthesiology and reanimation · 2026Article
- How large language models will be regulated in academia.Journal of anesthesia, analgesia and critical care · 2026Article
- Advances of regional nerve blocks for postoperative analgesia in laparoscopic gynecological surgery: precision, innovation, and future directions.Frontiers in medicine · 2026Review
- Artificial Intelligence and Large Language Models: Editorial Reflections.Turkish journal of anaesthesiology and reanimation · 2025Article
- Article
- Evaluation of two large language models for intensive care unit discharge decisions: a prospective observational cohort study.Brazilian journal of anesthesiology (Elsevier)Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly transforming anaesthesiology through advances in machine learning, deep learning, and large language models. AI-driven tools now contribute to nearly every phase of perioperative care, including preoperative risk stratification, intraoperative monitoring, imaging interpretation, airway assessment, regional anaesthesia, and critical care. Applications such as automated American Society of Anesthesiologists classification, prediction of postoperative complications and intensive care unit needs, electroencephalography-based depth-of-anaesthesia estimation, and proactive haemodynamic management are reshaping clinical decision-making. AI-augmented echocardiography enhances chamber recognition and functional measurements, whereas computer vision systems support airway evaluation and ultrasound-guided regional anaesthesia by providing real-time anatomical identification and facilitating training. In critical care, AI models facilitate the early detection of sepsis, organ dysfunction, and haemodynamic instability, while improving workflow efficiency and resource allocation. AI is increasingly used in academic writing, data processing, and medical education, offering opportunities for personalised learning and simulation but raising concerns about accuracy and hallucinations. In this review, we aimed to summarise the current applications of AI in anaesthesiology, highlight the methodological, ethical, and practical challenges that limit its integration, and discuss future directions for its safe and effective adoption in perioperative care.
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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.