Evidence map›Paper›PMID 41181061›Full record

ReviewHealth care science2025

RETRACTED: Artificial intelligence for emergency medical care.

Shivam Rajput, Pramod Kumar Sharma, Rishabha Malviya

RetractedAbstract readReviewRetracted Publication
In one paragraph

Review in Health care science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Shivam RajputDepartment of Pharmacy School of Medical and Allied Sciences, Galgotias University Greater Noida Uttar Pradesh India.
Pramod Kumar SharmaDepartment of Pharmacy School of Medical and Allied Sciences, Galgotias University Greater Noida Uttar Pradesh India.
Rishabha MalviyaDepartment of Pharmacy School of Medical and Allied Sciences, Galgotias University Greater Noida Uttar Pradesh India.ORCID 0000-0003-2874-6149

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is increasing research into the potential benefits of incorporating artificial intelligence (AI) and machine learning algorithms into emergency medical services. AI is finding new applications across a wide range of sectors, one of which is healthcare, where it is being used to enhance clinical diagnostics. AI solutions have enormous untapped potential to improve healthcare efficiency and quality, thus researchers have focused heavily on emergency medicine (EM). Many individuals without prior experience with any physician often receive their initial medical care in the emergency room. Two areas that could benefit from the implementation of AI are reducing waiting times and enhancing diagnostic capabilities. This study provides further explanation of how AI is used in emergency rooms. Several machine learning-based algorithms are also addressed. In this research, we summarise recent developments in the use of AI in EM. This research tries to summarise the usefulness of AI in EM by looking at recent developments in emergency department operations and clinical patient management.

Indexed as

algorithmartificial intelligenceemergency departmentemergency medicinemachine learning

Identifiers

PMID41181061
PMCPMC12574435

What OpenQuestion 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 OpenQuestion graph.