Evidence map›Paper›PMID 42846531›Full record

ArticleFrontiers in digital health2026

AI is already in the theatre of war just not on the side of the patient: clinical chatbots, telemedicine, and an agenda for "AI for medical humanity".

Andrew R Prigg, Henry Robinson, Munzer Alkhalil, James Gough

Abstract read
In one paragraph

Article in Frontiers in digital health, 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

4 authors.

Andrew R PriggSchool of Medicine and Population Health, Sheffield Medical school, Sheffield, United Kingdom.
Henry RobinsonSchool of Medicine and Population Health, Sheffield Medical school, Sheffield, United Kingdom.
Munzer AlkhalilDavid Nott Foundation, London, United Kingdom.
James GoughDavid Nott Foundation, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is already embedded in modern warfare, mainly through surveillance, targeting, and military decision-making. However, its potential to support clinicians and patients in conflict settings remains underdeveloped. Frontline medical teams must make time-critical, high-stakes decisions with scarce diagnostics, disrupted referrals, insecure communications, changing injury patterns, and irregular access to specialist expertise. Humanitarian medicine needs a proactive clinician-facing AI agenda that strengthens clinical judgment, not replaces it. We propose a two-stage model: a bounded, protocol-anchored chatbot as the "first mile" of point-of-care support, and telemedicine as the "second mile" for specialist oversight and accountability. The chatbot would structure assessments, guide protocol-based actions, identify missing information and areas of uncertainty, generate standard handovers, and trigger escalations. Remote specialists would provide context and retain responsibility for clinical decisions. Safe deployment requires transparent protocols, constrained scope, hard refusals, awareness of uncertainty, data minimisation, offline-first functionality, adversarial threat modelling, and safeguards against coercive or dual-use applications. Evaluation should use locally informed simulation, silent deployment, prospective monitoring of deviations and harms, predefined safety thresholds, and independent humanitarian oversight before wider clinical use. AI will increasingly shape conflict medicine, with or without humanitarian engagement. The priority is to ensure design, governance, and evaluation are guided by patient safety, medical neutrality, local involvement, and clinical accountability, placing AI on the side of the patient, not the machinery of war.

Indexed as

artificial intelligencechatbotclinical decision supporthumanitarian medicinelarge language model

Identifiers

PMID42846531
PMCPMC13642742

What OpenQuestion holds

Textmetadata
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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.