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About this paper
Georgianna Lin, Rencong Jiang, Noémie Elhadad, Xuhai "Orson" Xu
Researchers from Columbia University propose a multi-layer framework for designing longitudinal health AI agents, enabling sustained, coherent, adaptive, and patient-centered support across multiple sessions. This framework, grounded in principles of coherence, continuity, adaptation, and agency, is demonstrated through use cases in chronic symptom management, post-discharge follow-up, and mental health support.
Key insights
- Longitudinal health support by AI agents requires active design consideration for coherence, continuity, adaptation, and agency, rather than just expanded memory.
- Effective longitudinal agents must actively steward health goals, adapt to evolving circumstances, and manage the balance of authority with the user over time.
- Designing these systems involves inherent tensions, such as preserving past meaning while allowing revision and enabling proactivity without eroding user autonomy.
Summary by alphaXiv.
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