{
  "abstract": "Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive health AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary co-creation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy, held in the Netherlands. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive health AI. We identified three key challenges for realising inclusive health AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants’ perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and co-design approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, process, system and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal, and societal aspects when aiming to foster responsible and inclusive AI in healthcare. Author summary: Artificial intelligence (AI) is being used more and more in healthcare to improve diagnosis, treatment, and personalised care. However, if not designed carefully, these technologies can unintentionally increase existing inequalities and exclude certain groups from the benefits of these technologies. In our study, we brought together experts from healthcare, ethics, law, social sciences, and patient advocacy to explore how the application of AI in healthcare can be made more inclusive. During a five-day co-creation workshop in the Netherlands, we worked together to identify key issues and come up with practical solutions. We focused on three main areas: 1) Ensuring diverse voices are heard during the development of AI tools; 2) Making data collection fair and representative; and 3) Creating regulations that protect human rights. From the discussions of the workshop, we created the PREFER-IT framework, which outlines eight key principles for inclusive health AI: • Participatory and co-design approaches, • Representative and diverse data, • Education and digital literacy, • Fairness, • Ethical and legal accountability, • Real-world validation and feedback, • Inclusive communication, • Technical interoperability. This framework helps guide developers, policymakers, and healthcare professionals in creating AI systems that are not only effective but also fair and respectful of all users. Our work emphasises the importance of involving patients and communities in shaping the future of AI.",
  "abstract_shown": true,
  "affiliations": [
    "ELSA AI lab Northern Netherlands, Groningen, The Netherlands",
    "Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands",
    "Department of Health Psychology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands",
    "Department of Neonatology, Beatrix Children’s Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands",
    "Independent Researcher, Groningen, The Netherlands",
    "Department of Health Information Office, Information Management Healthcare, University Medical Center, University of Groningen, Groningen, The Netherlands",
    "STeP Research Group, Department of Transboundary Legal Studies, Faculty of Law, University of Groningen, Groningen, The Netherlands",
    "Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden",
    "Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands",
    "Faculty of Law, University of Groningen, Groningen, The Netherlands",
    "Department of Global Public Health and Bioethics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands",
    "Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands",
    "Department of Clinical Genetics, Erasmus Medical Centre, Rotterdam, The Netherlands",
    "Research Centre Art and Society, Hanze University of Applied Sciences, Groningen, The Netherlands",
    "Department for Human Machine Teaming, TNO, Soesterberg, The Netherlands",
    "Department of Primary and Community Care, Intellectual Disability and Health Group, Radboud University Medical Center, Nijmegen, The Netherlands",
    "Rathenau Instituut, Den Haag, The Netherlands",
    "Knowledge, Technology and Innovation Group, Wageningen University and Research, Wageningen, The Netherlands",
    "Department of Industrial Design Engineering, Delft University of Technology, Delft, The Netherlands",
    "Independent Artist, Amsterdam, The Netherlands",
    "Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands",
    "Research Group Digital Transformation, Hanze University of Applied Sciences, Groningen, The Netherlands",
    "AI Policy Lead, Ministry of the Interior and Kingdom Relations, Den Haag, The Netherlands",
    "Member of the ELSA Advisory Committee, The Hague, The Netherlands",
    "Unit of Computational Medicine and Neuropsychiatry, Faculty of Medicine, Pharmacy and Biomedical Sciences, University of Mons, Mons, Belgium",
    "Dutch Patient Federation, Utrecht, The Netherlands",
    "Department of Social Ethics, University of Bonn, Bonn, Germany",
    "Wellbeing and Healthcare Sector, AI Coalition for the Netherlands, Den Haag, The Netherlands",
    "Johannes Wier Foundation for Healthcare and Human Rights, Amsterdam, The Netherlands",
    "Ervaringsdeskundige/Expert by Experience, Sterk Uit Armoede, Haren, The Netherlands",
    "Ervaringsdeskundige/Expert by Experience, NALK (Netwerk Aanhoudende Lichamelijke Klachten), Deventer, The Netherlands",
    "National Institute for Health Research Biomedical Research Centre (Data, Health, and Society Theme), School of Primary Care, Population Sciences, and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom",
    "Department of Genetics and Data Science Center in Health, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands"
  ],
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      "name": "Špela Petrič"
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      "name": "Steff Groefsema"
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      "name": "Matias Valdenegro-Toro"
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      "name": "Matthias Braun"
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      "name": "Pieter Jeekel"
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  "published": "2026-09-23",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/fwmvph57jpy14.ris",
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  "title": "PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI",
  "title_text": "PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
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