Evidence map›Paper›PMID 42855718›Full record

ArticleAdvances in simulation (London, England)2026

The next frontier in healthcare simulation is human-AI teaming.

Sonia Jawaid Shaikh, Walter Eppich

Abstract readLetter
In one paragraph

Article in Advances in simulation (London, England), 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

2 authors.

Sonia Jawaid ShaikhSchool of Culture and Communication, The University of Melbourne, Melbourne, Victoria, Australia. soniajawaid.shaikh@unimelb.edu.au.ORCID http://orcid.org/0000-0002-3951-2025
Walter EppichDepartment of Medical Education and Collaborative Practice Centre, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcross health professions, AI agents are moving out of the background and into clinical teams as collaborative entities. Yet healthcare simulation continues to conceptualise AI as a tool that serves the scenario rather than one that inhabits it. This framing is no longer adequate. The clinical environments we are preparing practitioners for demand that professionals learn to team with AI and not merely use it. MAIN BODY: We contend that human-AI teaming (HAIT) is the next frontier for healthcare simulation education and research. The addition of AI agents to human teams fundamentally alters how teams communicate, work together, and accomplish goals and therefore, by extension, how we must design simulation systems for education and research. We identify five dimensions of teamwork that AI is reshaping and discuss what each means for simulation practice: communication and information exchange, shared mental models, trust calibration, team reflexivity, and team performance. We then present five imperatives that healthcare simulation must address to meet the challenges and opportunities HAIT introduces: fidelity, assessment, ethics, governance, and medico-legal framing, curriculum and faculty development, and transfer of learning. Together, these ten domains constitute a research and education agenda for a field built on the science and art of preparation.

conclusionHAIT is an emerging clinical reality and the next frontier for healthcare simulation. We call on the simulation community to treat it as a first-order priority and to focus on developing fidelity standards for AI agents in simulation, validating human-AI teaming assessment tools, establishing ethical frameworks for AI-inclusive scenario design, investing in curriculum and faculty development, and committing to the research needed to establish whether HAIT-based simulation training translates into improved performance in AI-augmented clinical practice.

Identifiers

PMID42855718
PMCPMC13651356

What OpenQuestion holds

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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.