TY  - JOUR
AU  - Peltner, Jonas
AU  - Becker, Cornelia
AU  - Wicherski, Julia
AU  - Wortberg, Silja
AU  - Aborageh, Mohamed
AU  - Costa, Inês
AU  - Ehrenstein, Vera
AU  - Fernandes, Joana
AU  - Heß, Steffen
AU  - Horváth-Puhó, Erzsébet
AU  - Korcinska Handest, Monika Roberta
AU  - Lentzen, Manuel
AU  - Maguire, Peggy
AU  - Meedom, Niels Henrik
AU  - Moore, Rebecca
AU  - Moore, Vanessa
AU  - Nagy, Dávid
AU  - McNamara, Hillary
AU  - Paakinaho, Anne
AU  - Pfeifer, Kerstin
AU  - Pylkkänen, Liisa
AU  - Rajamaki, Blair
AU  - Reviers, Evy
AU  - Röthlein, Christoph
AU  - Russek, Martin
AU  - Silva, Célia
AU  - De Valck, Dirk
AU  - Vo, Thuan
AU  - Bräuner, Elvira
AU  - Fröhlich, Holger
AU  - Furtado, Cláudia
AU  - Hartikainen, Sirpa
AU  - Kallio, Aleksi
AU  - Tolppanen, Anna-Maija
AU  - Haenisch, Britta
TI  - The EU project Real4Reg: unlocking real-world data with AI.
JO  - Health research policy and systems
VL  - 23
IS  - 1
SN  - 1478-4505
CY  - London
PB  - BioMed Central
M1  - DZNE-2025-00395
SP  - 27
PY  - 2025
AB  - 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.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.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).
KW  - Artificial Intelligence
KW  - European Union
KW  - Humans
KW  - Technology Assessment, Biomedical
KW  - Pharmacovigilance
KW  - Decision Making
KW  - Algorithms
LB  - PUB:(DE-HGF)16
C6  - pmid:40016823
C2  - pmc:PMC11869640
DO  - DOI:10.1186/s12961-025-01287-y
UR  - https://pub.dzne.de/record/277334
ER  -