Evidence map›Paper›PMID 41623804›Full record

ArticleBioinformation2025

Evaluation of AI tools for triage and risk stratification in emergency medicine.

Nikhil Paul, Parth Jani, Niyati Pandya, Amrit Podder, Mukul Singh, Carlos Andres Chango Rodriguez

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformation, 2025. 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

6 authors.

Nikhil PaulDepartment of Emergency Medicine, Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal, India.
Parth JaniDepartment of General Medicine, Government Medical College, Bhavnagar, Gujarat, India.
Niyati PandyaDepartment of Anaesthesiology, All India Institute of Medical Sciences, Rajkot, Gujarat, India.
Amrit PodderDepartment of Physiology, Teerthanker Mahaveer Medical College & Research Centre, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India.
Mukul SinghDepartment of General Surgery, All India Institute of Medical Sciences, Gorakhpur, Uttar Pradesh, India.
Carlos Andres Chango RodriguezDepartment of Medicine, Physician Armed Forces Hospital, Quito, Ecuador, South America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effectiveness of AI tools in triage and risk stratification in emergency medicine by comparing outcomes between AI-assisted triage and traditional triage methods is of interest. Hence, a total of 300 patients were enrolled, with 150 in each group, over 14 days. Results showed that the AI-assisted triage group had significantly reduced time to treatment, higher accuracy in triage decisions and more efficient resource allocation. Patient outcomes and length of stay were similar across both groups. Data shows that AI tools can enhance triage efficiency without compromising patient care.

Indexed as

AccuracyAI-assisted triageemergency medicinerisk stratificationtriage

Identifiers

PMID41623804
PMCPMC12859347

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Registered trials

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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.