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@ARTICLE{Peltner:277334,
      author       = {Peltner, Jonas and Becker, Cornelia and Wicherski, Julia
                      and Wortberg, Silja and Aborageh, Mohamed and Costa, Inês
                      and Ehrenstein, Vera and Fernandes, Joana and Heß, Steffen
                      and Horváth-Puhó, Erzsébet and Korcinska Handest, Monika
                      Roberta and Lentzen, Manuel and Maguire, Peggy and Meedom,
                      Niels Henrik and Moore, Rebecca and Moore, Vanessa and Nagy,
                      Dávid and McNamara, Hillary and Paakinaho, Anne and
                      Pfeifer, Kerstin and Pylkkänen, Liisa and Rajamaki, Blair
                      and Reviers, Evy and Röthlein, Christoph and Russek, Martin
                      and Silva, Célia and De Valck, Dirk and Vo, Thuan and
                      Bräuner, Elvira and Fröhlich, Holger and Furtado, Cláudia
                      and Hartikainen, Sirpa and Kallio, Aleksi and Tolppanen,
                      Anna-Maija and Haenisch, Britta},
      title        = {{T}he {EU} project {R}eal4{R}eg: unlocking real-world data
                      with {AI}.},
      journal      = {Health research policy and systems},
      volume       = {23},
      number       = {1},
      issn         = {1478-4505},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DZNE-2025-00395},
      pages        = {27},
      year         = {2025},
      abstract     = {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).},
      keywords     = {Artificial Intelligence / European Union / Humans /
                      Technology Assessment, Biomedical / Pharmacovigilance /
                      Decision Making / Algorithms},
      cin          = {AG Hänisch},
      ddc          = {610},
      cid          = {I:(DE-2719)1013010},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40016823},
      pmc          = {pmc:PMC11869640},
      doi          = {10.1186/s12961-025-01287-y},
      url          = {https://pub.dzne.de/record/277334},
}