Peltner, JonasJonasPeltnerBecker, CorneliaCorneliaBeckerWicherski, JuliaJuliaWicherskiWortberg, SiljaSiljaWortbergAborageh, MohamedMohamedAboragehCosta, InêsInêsCostaEhrenstein, VeraVeraEhrensteinFernandes, JoanaJoanaFernandesHess, SteffenSteffenHessHorvath-Puho, ErzsebetErzsebetHorvath-PuhoKorcinska Handest, Monika RobertaMonika RobertaKorcinska HandestLentzen, ManuelManuelLentzenMaguire, PeggyPeggyMaguireMeedom, Niels HenrikNiels HenrikMeedomMoore, RebeccaRebeccaMooreMoore, VanessaVanessaMooreNagy, DavidDavidNagyMcNamara, HillaryHillaryMcNamaraPaakinaho, AnneAnnePaakinahoPfeifer, KerstinKerstinPfeiferPylkkänen, Liisa H.Liisa H.PylkkänenRajamaki, BlairBlairRajamakiReviers, EvyEvyReviersRöthlein, ChristophChristophRöthleinRussek, MartinMartinRussekSilva, CéliaCéliaSilvade Valck, DirkDirkde ValckVo, ThuanThuanVoBräuner, ElviraElviraBräunerFröhlich, HolgerHolgerFröhlichFurtado, CláudiaCláudiaFurtadoHartikainen, Sirpa A.Sirpa A.HartikainenKallio, AleksiAleksiKallioTolppanen, Anna MaijaAnna MaijaTolppanenHaenisch, BrittaBrittaHaenisch2025-06-052025-06-052025https://publica.fraunhofer.de/handle/publica/48833110.1186/s12961-025-01287-y2-s2.0-8521963768940016823Background: 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).enfalsejournal article