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The review aims to map and describe the applications of generative and agentic AI in real-world evidence generation for healthcare research.

February 28, 2026Open Access

Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review

Key Points

The review aims to map and describe the applications of generative and agentic AI in real-world evidence generation for healthcare research.
Conduct a Living Scoping Review (LSR) focusing on genAI and agentic AI applications in RWE generation.
Perform systematic searches in databases like Embase and MEDLINE, including grey literature.
Two senior reviewers will screen citations, supplemented by a commercial screening algorithm.
Data extraction will be managed by one reviewer, with a 10% quality check by another.
Use descriptive statistics and thematic analysis for summarizing and identifying patterns, gaps, and opportunities.
The review will catalog current applications of genAI in specific RWE research tasks.
It will identify gaps in knowledge and areas ripe for further exploration.
Findings will provide insights into generative AI models' effectiveness in healthcare applications.

Abstract

Introduction 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. 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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