Evidence map›Paper›PMID 39719381›Full record

ReviewBMJ military health2025

Give us a hand, mate! A holistic review of research on human-machine teaming.

Jitu Patel, M Boardman, B Files, F Gregory, S Lamb, S Sarkadi, M Tešić, N Yeung

Abstract readReview
In one paragraph

Review in BMJ military health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. 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

8 authors.

Jitu PatelDSTL, Salisbury, UK jmpatel@dstl.gov.uk.ORCID http://orcid.org/0009-0009-4926-0712
M BoardmanDSTL, Salisbury, UK.
B FilesUS Army Research Laboratory, Aberdeen Proving Ground, Maryland, USA.
F GregoryUS Army Research Laboratory, Aberdeen Proving Ground, Maryland, USA.
S LambMOD, London, UK.
S SarkadiKing's College London, London, UK.
M TešićBirkbeck University of London, London, UK.
N YeungUniversity of Oxford Social Sciences Division, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Defence has a significant interest in the use of artificial intelligence (AI)-based technologies to address some of the challenges it faces. At the core of future military advantage will be the effective integration of humans and AI into human-machine teams (HMT) that leverages the capabilities of people and technologies to outperform adversaries. Realising the full potential of these technologies will depend on understanding the relative strengths of humans and machines, and how we design effective integration to optimise performance and resilience across all use cases and environments.Since the first robot appeared on the assembly line, machines have effectively augmented human capability and performance; however, they fall short of being a team member-someone you can ask to give you a hand! Working in teams involves collaboration, adaptive and dynamic interactions between team members to achieve a common goal. Currently, human-machine partnership is typically one of humans and machines working alongside each other, with each conducting discrete functions within predicable process and environments. However, with recent advances in neuroscience and AI, we can now envisage the possibility of HMT, not just in physical applications, but also complex cognitive tasks.This paper provides a holistic review of the research conducted in the field of HMT from experts working in this area. It summarises completed and ongoing studies and research in the UK and USA by a broad group of researchers. This work was presented in the HMT thematic session at the Sixth International Congress on Soldiers' Physical Performance (ICSPP23 London).

Indexed as

Artificial IntelligenceCooperative BehaviorMan-Machine SystemsMilitary PersonnelHumansBASIC SCIENCESBIOTECHNOLOGY & BIOINFORMATICSInformation technology

Identifiers

PMID39719381
PMCPMC12505082

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.