ReviewJournal of cardiothoracic and vascular anesthesia2024
Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.
Review in Journal of cardiothoracic and vascular anesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Computational Approaches to Penicillin Allergy De-labelling: A Clinical Review.Clinical reviews in allergy & immunology · 2026Review
- Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.Scientific reports · 2026Article
- Random forest-based prediction of early postoperative pain after pelvic organ prolapse surgery under standardized remimazolam anesthesia.BMC anesthesiology · 2026Observational
- Artificial Intelligence in Anesthesia: Enhancing Precision, Safety, and Global Access Through Data-Driven Systems.Journal of clinical medicine · 2025Review
- What is new in cardiac anesthesia in 2024?Journal of anesthesia and translational medicine · 2025Review
- Intraoperative Opioid Administration Variation in Cardiac Surgery: Predictable, Yet Not Personalized.Anesthesia and analgesia · 2025Article
- A review of recent advances in anesthetic drugs for patients undergoing cardiac surgery.Frontiers in pharmacology · 2025Review
- The anesthesiologist's guide to critically assessing machine learning research: a narrative review.BMC anesthesiology · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.
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.