Artificial intelligence in critical synthesis of public health responses to violence: A novel application to UK violence prevention policy

Cook, D ORCID: 0000-0002-6810-0281, Cook, E ORCID: 0000-0002-7608-8702, Cullen, K, Zachos, K, McManus, S ORCID: 0000-0003-2711-0819, Bellis, MA ORCID: 0000-0001-6980-1963, Feder, GS ORCID: 0000-0002-7890-3926 and Maiden, N (2026) Artificial intelligence in critical synthesis of public health responses to violence: A novel application to UK violence prevention policy. Public Health, 255. ISSN 0033-3506

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Abstract

Objectives
Artificial intelligence (AI) systems are increasingly applied in public health, yet their use for analysing fragmented, multi-sectoral policy landscapes remains underdeveloped. This study aimed to describe the development and preliminary exploration of an AI-enabled tool designed to synthesise evidence from violence-related policy documents in the UK.

Study design
An exploratory, proof-of-concept case study.

Methods
A corpus of publicly available UK policy and strategy documents on violence (N = 343) was compiled through expert review, manual searches of government and third sector organisation websites, and automated web scraping. We used the corpus to train an existing AI framework and deployed it through a question-answer interface. Stakeholders were invited to pose natural-language questions about violence policy and consider the system's utility and the usefulness of its outputs.

Results
Stakeholders reported that the AI-enabled tool facilitated flexible interrogation of violence-related policy documents and supported identification of recurring framings, sectoral differences, and potential policy siloes. Feedback indicated that the system improved the efficiency and transparency of cross-sectoral policy analysis, particularly in the initial stages of an inquiry.

Conclusions
This short communication provides early insight into the potential of AI-enabled tools to support public health policy analysis by structuring and synthesising complex documentary evidence. Such functionality is particularly relevant in areas requiring cross-sectoral collaboration. Further work is required to formally evaluate performance, assess bias, and explore impacts of AI on real-world decision-making prior to wider implementation.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence; Evidence synthesis; Large language models; Policy analysis; Public health policy; Violence and abuse; 4202 Epidemiology; 4203 Health Services and Systems; 4206 Public Health; 42 Health Sciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; 8.1 Organisation and delivery of services; Generic health relevance; 16 Peace, Justice and Strong Institutions; 1117 Public Health and Health Services; Public Health; 4202 Epidemiology; 4203 Health services and systems; 4206 Public health
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine
Divisions:	Public and Allied Health
Publisher:	Elsevier BV
Date of acceptance:	17 March 2026
Date of first compliant Open Access:	1 May 2026
Date Deposited:	01 May 2026 12:38
Last Modified:	01 May 2026 12:38
DOI or ID number:	10.1016/j.puhe.2026.106258
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28503

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