Evidence map›Paper›PMID 41245193›Full record

ArticleFrontiers in robotics and AI2025

Pathways to family-centered healthcare: co-designing AI solutions with families in pediatric rehabilitation.

Silvia Filogna, Giovanni Arras, Tommaso Turchi, Giuseppe Prencipe, Elena Beani, Clara Bombonato, Francesca Fedeli, Gemma D'Alessandro, Antea Scrocco, Giuseppina Sgandurra

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Silvia FilognaDepartment of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy.
Giovanni ArrasDepartment of Computer Science, University of Pisa, Pisa, Italy.
Tommaso TurchiDepartment of Computer Science, University of Pisa, Pisa, Italy.
Giuseppe PrencipeDepartment of Computer Science, University of Pisa, Pisa, Italy.
Elena BeaniDepartment of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy.
Clara BombonatoDepartment of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy.
Francesca FedeliFightTheStroke Foundation, Milan, Italy.
Gemma D'AlessandroFTS srl, Milan, Italy.
Antea ScroccoDepartment of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy.
Giuseppina SgandurraDepartment of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

artificial intelligencecerebral palsyethical AIfamily-centered designhealthcare technologyparticipatory designpediatric rehabilitationstakeholder perspectives

Identifiers

PMID41245193
PMCPMC12611681

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.