PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI

@article{ferreira2026preferit,
  title = {{PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI}},
  author = {Patrícia Pita Ferreira and Sara Soriano Longarón and Wiam Bouisaghouane and Jetse Goris and Anne H. Hoekman and Balázs Markos and Benjamin Maus and Giorgia Pozzi and Hadi Hasan and Indre Kalinauskaité and Jonáh Stunt and Joosje D. Kist and Judith van der Elst and Katell Maguet and Liv Ziegfeld and Maarten Cuypers and Megan Milota and Michelle Habets and Sara Colombo and Špela Petrič and Steff Groefsema and Steven Warmelink and Elja Daae and Giovanni Briganti and Ildikó Vajda and Matias Valdenegro-Toro and Matthias Braun and Pieter Jeekel and Simone Goosen and Alex Schepel and Laxmie Ester and Riane Kuzee and Sophie de Klerk and Claudine Lamoth and Lisa Ballard and Mirjam Plantinga},
  journal = {PLOS Digital Health},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pdig.0001096},
  url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001096},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI
AU  - Patrícia Pita Ferreira
AU  - Sara Soriano Longarón
AU  - Wiam Bouisaghouane
AU  - Jetse Goris
AU  - Anne H. Hoekman
AU  - Balázs Markos
AU  - Benjamin Maus
AU  - Giorgia Pozzi
AU  - Hadi Hasan
AU  - Indre Kalinauskaité
AU  - Jonáh Stunt
AU  - Joosje D. Kist
AU  - Judith van der Elst
AU  - Katell Maguet
AU  - Liv Ziegfeld
AU  - Maarten Cuypers
AU  - Megan Milota
AU  - Michelle Habets
AU  - Sara Colombo
AU  - Špela Petrič
AU  - Steff Groefsema
AU  - Steven Warmelink
AU  - Elja Daae
AU  - Giovanni Briganti
AU  - Ildikó Vajda
AU  - Matias Valdenegro-Toro
AU  - Matthias Braun
AU  - Pieter Jeekel
AU  - Simone Goosen
AU  - Alex Schepel
AU  - Laxmie Ester
AU  - Riane Kuzee
AU  - Sophie de Klerk
AU  - Claudine Lamoth
AU  - Lisa Ballard
AU  - Mirjam Plantinga
PY  - 2026
DA  - 2026/09/23
JO  - PLOS Digital Health
DO  - 10.1371/journal.pdig.0001096
UR  - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001096
AB  - 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.
SN  - 2767-3170
ER  -