Pathways to Family-Centered Healthcare: Co-Designing AI Solutions with Families in Pediatric Rehabilitation

Silvia Filogna^1*Giovanni Arras²Tommaso Turchi^2*Giuseppe Prencipe²Elena Beani^1,3Clara Bombonato¹Francesca Fedeli⁴Gemma D'Alessandro⁵Antea Scrocco^1,6Giuseppina Sgandurra^1,3

• ¹Department of Developmental Neuroscience, Stella Maris Scientific Institute, University of Pisa, Pisa, Tuscany, Italy
• ²Department of Computer Science, University of Pisa, Pisa, Tuscany, Italy
• ³Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Tuscany, Italy
• ⁴Fightthestroke Foundation, Milan, Italy
• ⁵FTS srl, Milan, Italy
• ⁶Department of Neuroscience, Psychology, Medication Area and Child Health (NEUROFARBA), University of Florence, Florence, Italy

Despite the growing interest in Artificial Intelligence (AI) for pediatric rehabilitation, family engagement in the technologies design remains limited. Understanding how AI-driven tools align with family needs, caregiving routines, and ethical concerns is crucial for their successful adoption. In this study, we actively involved nine families of children with Cerebral Palsy (CP) in an online participatory design workshop, underscoring both the feasibility and the need of integrating family's perspectives into AI development. Families enthusiastically participated, not only sharing insights but also appreciating the opportunity to contribute to shaping future technologies. Their active engagement challenges the assumption that co-design with families is complex or impractical, highlighting how structured yet flexible methodologies can make such crucial initiatives highly effective. The online format further facilitated participation, allowing families to join the discussion and ensuring a diverse range of perspectives. The workshop's key findings reveal three core priorities for families: (1) AI should adapt to daily caregiving routines rather than impose rigid structures; (2) digital tools should enhance communication and collaboration between families and clinicians, rather than replace human interaction; and (3) AI-driven systems could empower children's autonomy while maintaining parental oversight. Additionally, families raised critical concerns about data privacy, transparency, and the need to preserve empathy in AI-mediated care. Our findings reinforce the urgent need to shift toward family-centered AI design, moving beyond purely technological solutions toward ethically responsible, inclusive innovations. This research not only demonstrates the possibility and success of engaging families in co-design processes but also provides a model for future AI development that genuinely reflects the lived experiences of children and caregivers.