Evidence map›Paper›PMID 38406336›Full record

ArticleIndian journal of anaesthesia2024

Role of artificial intelligence in haemodynamic monitoring.

Sheila N Myatra, Bharat G Jagiasi, Neeraj P Singh, Jigeeshu V Divatia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Indian journal of anaesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Foundations and Advancements in Hemodynamic Monitoring: Part I-Elements of HemodynamicsTurkish journal of anaesthesiology and reanimation · 2025
    Article
  4. Article
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

4 authors.

Sheila N MyatraDepartment of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.ORCID https://orcid.org/0000-0001-6761-163X
Bharat G JagiasiDirector of Critical Care Department, Kokilaben Dhirubhai Ambani Hospital, Navi Mumbai, Maharashtra, India.ORCID https://orcid.org/0000-0002-3068-1201
Neeraj P SinghDepartment of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.ORCID https://orcid.org/0000-0003-1617-7735
Jigeeshu V DivatiaDepartment of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.ORCID https://orcid.org/0000-0001-7384-4886

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care. The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWS

Indexed as

Artificial intelligenceearly warning signshaemodynamic monitoringhypotension prediction indexmachine learning

Identifiers

PMID38406336
PMCPMC10893816

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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 OpenQuestion graph.