A longitudinal health agent framework
career value
197K/year
🤖 AI Summary
Current AI health agents struggle to support long-term health tasks, primarily due to insufficient understanding of user intent, weak goal alignment, and a lack of sustained accountability mechanisms. This work proposes a multilayer agent architecture grounded in both clinical and personal health informatics principles, integrating adaptive reasoning, contextually coherent modeling, dynamic goal tracking, and user-centered interaction to ensure coherence, continuity, and user autonomy across multi-turn dialogues. The framework effectively addresses enduring needs such as symptom management and behavior change, demonstrating in representative use cases its capacity to sustain user engagement, dynamically adapt to evolving goals, and support personalized, safe decision-making. This approach offers a novel design paradigm and practical guidance for developing multi-session health AI systems.
Application Category
📝 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. Through representative use cases, we demonstrate how longitudinal agents can maintain meaningful engagement, adapt to evolving goals, and support safe, personalized decision-making over time. Our findings underscore both the promise and the complexity of designing systems capable of supporting health trajectories beyond isolated interactions, and we offer guidance for future research and development in multi-session, user-centered health AI.
Problem
Research questions and friction points this paper is trying to address.
longitudinal health
AI agents
user intent
accountability
health trajectories
Innovation
Methods, ideas, or system contributions that make the work stand out.
longitudinal AI agents
health informatics
adaptive interaction
coherent reasoning
user agency
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