Evidence map›Paper›PMID 42433338›Full record

ReviewAvicenna journal of medicine2026

Adopting AI Enhances Humanitarian Operations While Demanding Critical Trade-Offs.

Caroline Grangier, Munzer Alkhalil, Aula Abbara

Abstract readReview
In one paragraph

Review in Avicenna journal of medicine, 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

3 authors.

Caroline GrangierAntei Global, Paris, France.ORCID 0009-0008-8869-4591
Munzer AlkhalilResearch for Health System Strengthening in Northern Syria (R4HSSS), The Centre for Conflict & Health Research (CCHR), King's College London, London, United Kingdom.ORCID 0000-0003-2156-8802
Aula AbbaraDepartment of Infection, Imperial College London, London, United Kingdom.ORCID 0000-0002-7049-8399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly impacting the cooperation sector, transforming how humanitarians are operating in the field. AI tools now allow for improved health diagnostics and quality services. In conflict areas, organizations are able to improve the efficiency of their analysis, and propose optimized data management processes for more multifactorial predictions. In a context where human and financial resources are limited, AI address the gaps to sustain emergency responses. In this commentary, we explore the trade-offs of adopting those tools and the ethical concerns their usages raise in a fragile environment. Security breach due to human errors and unregulated data management are a substantial risk to the humanitarian workers but also to the communities they aim to protect via their interventions. In this work, we touch upon humanitarians' concerns with this new technology, and reflect on balancing those with the benefits of accuracy of interventions. We argue that the humanitarian sector must not overlook these technologies, as failing to adopt them, risks widening inequalities in access to data-driven decision-making and leaving vulnerable systems further behind. At the same time, it is essential to establish meaningful safeguards and promote local ownership to ensure that AI enhances resilience, rather than reinforcing existing power imbalances.

Indexed as

AIconflict zonesethical considerationshealthhumanitarianstrade-off

Identifiers

PMID42433338
PMCPMC13354509

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.