ArticleBMJ open2026
Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Prospective evaluation of a large language model clinical decision support system in the emergency department.Nature medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial 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. METHODS AND ANALYSIS: 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). ETHICS AND DISSEMINATION: Ethical approval is not required due to use of previously published data. Planned dissemination includes peer-reviewed publication, presentation and short summaries.
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Registered trials
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.