Artificial intelligence (AI) is rapidly evolving, offering an expanding suite of capabilities that go beyond the traditional focus on prediction and classification. Generative AI (GenAI) and agentic AI could create transformative practices to support real-world evidence (RWE) generation for health research by streamlining studies, accelerating insights and improving decision-making. However, there is no published overview available describing the range of applications in RWE generation. This review aims to describe where and how genAI and agentic AI are applied across the domains of healthcare research tasks for RWE generation. Additionally, to map applications by tasks and methods across the product lifecycle continuum, and to identify emerging gaps and opportunities.

This Living Scoping Review (LSR) will include studies reporting an application and/or evaluation of genAI or agentic AI applied to one or more RWE generation research tasks. Searches will be conducted in Embase, MEDLINE and additional sources (eg, grey literature). Citations will be independently screened by two human senior reviewers for a substantive training dataset and a commercially available screening algorithm (Robot Screener) will complete screening with a human reviewer. The LSR will include reports of studies (primary or reviews) describing and/or evaluating the application of any genAI model for RWE generation in healthcare, in English, published from 1 January 2025 to the date of search. Data will be extracted from all studies included in the LSR by one independent senior reviewer using a piloted template, with 10% quality check by a second senior reviewer. Descriptive statistics will be used to summarise the applications of genAI per RWE research task, and the results of genAI evaluations. Thematic analysis will be used to describe genAI application patterns, trends, gaps and opportunities. The LSR protocol and reports will be updated annually, and findings will be published on a publicly available website (eg, ISPE—the International Society for Pharmacoepidemiology).

Ethical approval is not required due to use of previously published data. Planned dissemination includes peer-reviewed publication, presentation and short summaries.

Urgent and emergency care (UEC) systems in England face unprecedented pressures, with record accident and emergency attendances, persistent breaches of ambulance response targets and poorer outcomes for time-sensitive conditions. National UEC recovery plans have introduced multiple innovations—such as same-day emergency care, virtual wards and specialty hubs—to manage these pressures and improve patient flow. Rural coastal areas are particularly vulnerable to excessive demand due to higher levels of deprivation, older populations with complex health needs, seasonal surges that generate unpredictable demand and challenges in attracting and retaining staff. Following the Chief Medical Officer’s 2021 Annual Report, funding research and developing bespoke solutions to manage UEC demand and address geographical disparities has been recognised as a national priority. The Elevate study responds to this priority by identifying and evaluating innovative models of UEC in rural coastal communities in England.

The Elevate study is a 30-month, mixed-methods evaluation that comprises three interlinked work packages: (1) National service mapping—outlining provision of innovative models of UEC in rural coastal areas of England. This will be developed through document review and interviews with regional and national service leaders. (2) Quantitative analysis—quasiexperimental and longitudinal approaches will use National Health Service (NHS) England’s Emergency Care Data Set and linked routine NHS datasets to evaluate the impact of UEC models on health and process outcomes. Standard and bespoke metrics will be developed and used to assess performance. (3) Qualitative case studies—up to 12 case studies of UEC models in rural coastal communities. Interviews with patients and staff and non-participant observation will explore how and why different UEC models influence patient experience, clinical outcomes, resource use and the workforce. Findings will be integrated using the Consolidated Framework for Implementation Research to identify components of UEC models that are effective, scalable and sensitive to local context,

Ethical approval for qualitative components was granted by the North of Scotland Research Ethics Committee (25/NS/0099). Dissemination will include peer-reviewed publications, policy briefs, creative media and community engagement activities to ensure findings are communicated inclusively and effectively to policymakers, health and social care practitioners and the public.

Research Registry (researchregistry11126).

Nicotine replacement therapy (NRT) helps pregnant women quit smoking. Usual National Health Service (NHS) cessation care in pregnancy starts only after women stop smoking and comprises behavioural support and NRT. NRT is stopped if women restart smoking. We hypothesised that NRT would have a bigger effect on cessation in pregnancy if used: (1) to reduce smoking before quitting (‘preloading’), (2) during brief smoking lapses after quitting and (3) to help those who cannot stop smoking, to reduce instead.

A two-arm parallel group, open-label, multicentre, assessor-blind randomised controlled trial. Participants are recruited at hospital antenatal clinics and other NHS settings throughout England and Wales or via social media advertising. Those enrolled are in antenatal care,

Ethics approval was granted by the West Midlands—Coventry & Warwickshire Research Ethics Committee (REC reference: 21/WM/0172; Protocol number 21001; IRAS Project ID: 291236). Written informed consent will be obtained from all participants. Findings will be disseminated to the public, funders, relevant practice and policy representatives and other researchers.

ISRCTN84798566.