Rethinking Health Agents: From Siloed AI to Collaborative Decision Mediators

Researchers propose a new framework for AI health agents that moves away from siloed, individual-user systems toward collaborative decision mediators that work within multi-stakeholder healthcare relationships. The study demonstrates through a pediatric case study that current AI tools fail to address collaboration gaps between patients, caregivers, and clinicians, proposing instead AI systems that preserve human authority while facilitating shared understanding.

• →Current AI health agents operate in silos, supporting individual users rather than multi-stakeholder healthcare relationships.
• →Fragmented AI use can worsen misalignment between patients, caregivers, and clinicians in healthcare decisions.
• →A pediatric chronic kidney disease case study revealed that treatment adherence issues stem from fragmented situational awareness and misaligned goals.
• →General-purpose AI tools currently do little to bridge collaboration gaps in healthcare settings.
• →The proposed framework focuses on AI as collaborators that surface context, reconcile mental models, and scaffold shared understanding while preserving human decision authority.