A Longitudinal Health Agent Framework
Summary: arXiv:2604.12019v1 Announce Type: new
Abstract
Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, behavior change, and patient support, most current implementations fall short of facilitating user intent and fostering accountability. This contrasts with prior work on supporting longitudinal needs, where follow-up, coherent reasoning, and sustained alignment with individuals’ goals are critical for both effectiveness and safety.
In this paper, we draw on established clinical and personal health informatics frameworks to define what it would mean to orchestrate longitudinal health interactions with AI agents. We propose a multi-layer framework and corresponding agent architecture that operationalizes adaptation, coherence, continuity, and agency across repeated interactions.
Key Findings
- Engagement: Longitudinal agents can maintain meaningful engagement with users over extended periods.
- Adaptation: The agents are designed to adapt to the evolving goals of users, offering personalized support that aligns with individual health trajectories.
- Decision-Making: They support safe and personalized decision-making, addressing the need for accountability in health management.
Framework Overview
The proposed framework emphasizes the need for AI agents to not only interact with users but to do so in a manner that is coherent and responsive to their needs. The multi-layer framework includes:
- Adaptation Layer: This layer focuses on the agent’s ability to learn from interactions and adjust its responses based on user feedback and changing health conditions.
- Coherence Layer: Ensures that interactions are logically connected, allowing users to build on previous conversations and experiences.
- Continuity Layer: Facilitates ongoing support by maintaining context over time, which is essential for health management.
- Agency Layer: Empowers users by involving them in the decision-making process, ensuring that their choices are respected and prioritized.
Implications for Future Research
The findings from this research underscore both the promise and complexity of designing systems capable of supporting health trajectories beyond isolated interactions. As AI continues to evolve, it is crucial to focus on user-centered design principles that prioritize accountability and engagement. Future research and development should explore:
- Enhancing user feedback mechanisms to improve the adaptation of AI agents.
- Investigating the ethical implications of AI in health decision-making.
- Developing strategies for integrating longitudinal health data with AI systems.
In conclusion, this paper lays the groundwork for a new paradigm in health AI, advocating for systems that are not only intelligent but also empathetic and supportive of users’ long-term health journeys.