Evidence map›Paper›PMID 41647726›Full record

ArticleAI and ethics2026

An AI ethics framework for a trustworthy autonomous drone system to support battlefield casualty triage.

Peter Lee, Tasweer Ahmad, Syed Mohammad Waheed, Andrew Kenning

Abstract read
In one paragraph

Article in AI and ethics, 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.

Peter LeeCentre for Defence, Risk and Resilience, University of Portsmouth, Portsmouth, United Kingdom.
Tasweer AhmadDepartment of Computer Science, Edge Hill University, Ormskirk, United Kingdom.
Syed Mohammad WaheedAdvanced Engineering Centre, University of Brighton, Brighton, United Kingdom.
Andrew KenningCentre for Defence, Risk and Resilience, University of Portsmouth, Portsmouth, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AI-enabled capabilities in war provide new ethical challenges, even for nonlethal support tools such as the battlefield casualty triage drones that are the focus of this paper. We address an important and underexplored problem: how to embed ethical considerations into military AI systems that are designed to save lives rather than take them. The paper examines the 'ATRACT' project, which is developing an AI-powered drone as a trustworthy robotic autonomous system (RAS) to help frontline medics prioritise casualties in the critical post-trauma minutes that shape survival chances. As a position paper written while development is still underway, it includes the bespoke ethics framework created in the course of the project to date and offers real-time insights for other defence and security projects seeking to operationalise abstract AI ethics principles into concrete design and assurance guidance. We examine and draw upon approaches to operationalizing abstract principles in adjacent domains, to show how high-level principles can be translated into implementable requirements for technical robustness, ethical compliance, safety, and legal conformity, actively shaping system architecture, data, and human-machine interaction. We argue that trustworthiness is a socio-technical property that emerges from governance, documentation, and oversight rather than code alone, and that ethical assurance for triage drones must be designed in from inception and verified through ongoing testing, audit, and transparent evidence of due diligence. Supplementary Information: The online version contains supplementary material available at 10.1007/s43681-025-00967-3.

Indexed as

AI-powered autonomous systemsEthical challengesEthical principlesPost-trauma careRobotic autonomous systemSocio-technical property

Identifiers

PMID41647726
PMCPMC12868081

What OpenQuestion holds

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LicenceCC BY
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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.