Evidence map›Paper›PMID 39659330›Full record

ReviewCureus2024

Harnessing Artificial Intelligence (AI) in Anaesthesiology: Enhancing Patient Outcomes and Clinical Efficiency.

Arnesh Shukla, Ayesha Salma, Dev Patel, Jabez David John, Reshmitha Kantamneni, Tirath Patel, Ketan Kantamaneni

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Arnesh ShuklaPsychiatry, St. Martinus University, Foster City, USA.
Ayesha SalmaInternal Medicine, Shadan Institute of Medical Sciences, Hyderabad, IND.
Dev PatelInternal Medicine, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, IND.
Jabez David JohnSurgery, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA.
Reshmitha KantamneniMedicine, Rangaraya Medical College, Kakinada, IND.
Tirath PatelMedicine, American University of Antigua, St. John, ATG.
Ketan KantamaneniTrauma and Orthopaedics, East Kent University Hospitals NHS Foundation Trust, Ashford, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

aianesthesiologyartificial intelligence (ai)clinical efficiencypatient outcomes

Identifiers

PMID39659330
PMCPMC11631157

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.