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. 2025 Feb 27;23(1):27.
doi: 10.1186/s12961-025-01287-y.

The EU project Real4Reg: unlocking real-world data with AI

Jonas Peltner  1 , Cornelia Becker  2 , Julia Wicherski  2 , Silja Wortberg  2 , Mohamed Aborageh  3 , Inês Costa  4 , Vera Ehrenstein  5 , Joana Fernandes  6 , Steffen Heß  7 , Erzsébet Horváth-Puhó  5 , Monika Roberta Korcinska Handest  8 , Manuel Lentzen  3 , Peggy Maguire  9 , Niels Henrik Meedom  8 , Rebecca Moore  9 , Vanessa Moore  9 , Dávid Nagy  5 , Hillary McNamara  9 , Anne Paakinaho  10 , Kerstin Pfeifer  2 , Liisa Pylkkänen  11 , Blair Rajamaki  10 , Evy Reviers  12 , Christoph Röthlein  2 , Martin Russek  2 , Célia Silva  6 , Dirk De Valck  12 , Thuan Vo  10 , Elvira Bräuner  8 , Holger Fröhlich  3   13 , Cláudia Furtado  6   14 , Sirpa Hartikainen  10 , Aleksi Kallio  15 , Anna-Maija Tolppanen  10 , Britta Haenisch  16   17   18
Affiliations

The EU project Real4Reg: unlocking real-world data with AI

Jonas Peltner et al. Health Res Policy Syst. .

Abstract

Background: The use of real-world data is established in post-authorization regulatory processes such as pharmacovigilance of drugs and medical devices, but is still frequently challenged in the pre-authorization phase of medicinal products. In addition, the use of real-world data, even in post-authorization steps, is constrained by the availability and heterogeneity of real-world data and by challenges in analysing data from different settings and sources. Moreover, there are emerging opportunities in the use of artificial intelligence in healthcare research, but also a lack of knowledge on its appropriate application to heterogeneous real-world data sources to increase evidentiary value in the regulatory decision-making and health technology assessment context.

Methods: The Real4Reg project aims to enable the use of real-world data by developing user-friendly solutions for the data analytical needs of health regulatory and health technology assessment bodies across the European Union. These include artificial intelligence algorithms for the effective analysis of real-world data in regulatory decision-making and health technology assessment. The project aims to investigate the value of real-world data from different sources to generate high-quality, accessible, population-based information relevant along the product life cycle. A total of four use cases are used to provide good practice examples for analyses of real-world data for the evaluation and pre-authorization stage, the improvement of methods for external validity in observational data, for post-authorization safety studies and comparative effectiveness using real-world data. This position paper introduces the objectives and structure of the Real4Reg project and discusses its important role in the context of existing European projects focussing on real-world data.

Discussion: Real4Reg focusses on the identification and description of benefits and risks of new and optimized methods in real-world data analysis including aspects of safety, effectiveness, interoperability, appropriateness, accessibility, comparative value creation and sustainability. The project's results will support better decision-making about medicines and benefit patients' health. Trial registration Real4Reg is registered in the HMA-EMA Catalogues of real-world data sources and studies (EU PAS number EUPAS105544).

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The four Real4Reg use cases
Fig. 2
Fig. 2
Machine Learning methods used in Real4Reg
See this image and copyright information in PMC

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