ReviewCureus2024
Harnessing Artificial Intelligence (AI) in Anaesthesiology: Enhancing Patient Outcomes and Clinical Efficiency.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
The rapid rise and potential of artificial intelligence (AI) have created growing excitement and much debate on its potential to bring transformative changes across entire industries, including the medical industry. This systematic review aims to investigate the advancements in the AI industry and its potential implementation, specifically in the field of anaesthesiology. AI has already been integrated into different areas of medicine, including diagnostic uses in radiology and pathology and therapeutic and interventional uses in cardiology and surgery. In the field of anaesthesiology, AI has made significant progress. Potential applications include personalised drug dosing, real-time monitoring of vital signs, automated anaesthesia delivery systems, and predictive analytics for adverse events. As AI technologies continue to advance and become more prevalent in medicine, clinicians across all specialities need to understand these technologies and how they can be utilised to provide safer and more efficient care. With the rapid evolution of AI and the introduction of new concepts such as machine learning (ML), deep learning (DL), and neural networks, the field of anaesthesiology is set to undergo transformative changes. In this systematic review, we examine the existing literature to explore the current state of AI in the field of anaesthesiology, along with a prospective look at potential applications in the future. Along with its various applications, we will also discuss its limitations and flaws. As the field progresses, it is crucial to thoughtfully examine the ethical aspects of using AI in anaesthesia and ensure these technologies are applied responsibly and transparently.
Indexed as
Identifiers
What Socratic 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 Socratic graph.