Evidence map›Paper›PMID 41858902›Full record

ArticleJournal of the American College of Emergency Physicians open2026

The Future of Artificial Intelligence in Emergency Medical Services by 2030: An International Consensus Report.

Talal AlShammari

Abstract read
In one paragraph

Article in Journal of the American College of Emergency Physicians open, 2026. 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

1 author.

Talal AlShammariDepartment of Emergency Medical Care, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Artificial intelligence (AI) is an application of digital technology with considerable potential to improve quality, safety, and access; however, research on the future of AI in the health-care system and specifically emergency medical services (EMS) remains limited. The aim of this research is to anticipate the possible effects of using this technology on EMS. Methods: A Delphi panel was recruited based on proven international expertise in EMS to contribute to an iterative consensus-based Delphi study design. Data collection was initiated in September 2024 and ended in March 2025. Results: The 3-round study involved 27 expert participants and a 3-member steering group and was concluded at round 3, with 7 items not reaching consensus. The final result included 81 items with expert consensus divided into 6 EMS domains, namely communication, clinical, education, management, operations, and ethics. Conclusion: The forecasted items, although with a limited number of experts, provide an insightful model for an in-depth understanding of the future of AI in EMS and other health care, educational, and associated industries.

Indexed as

AIartificial intelligenceEMS systemskey performance indicator

Identifiers

PMID41858902
PMCPMC12996645

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.