# A Framework for Longitudinal Health AI Agents

arXiv: https://arxiv.org/abs/2604.12019 · HTML: https://mlsanity.com/paper/2604.12019/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 80 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [A Survey on Multi-Turn Interaction Capabilities of Large Language Models](https://mlsanity.com/paper/2501.09959/) (2025)
- [GPTCoach: Towards LLM-Based Physical Activity Coaching](https://mlsanity.com/paper/2405.06061/) (2024)
- [TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues](https://mlsanity.com/paper/2502.01630/) (2025)
- [Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation](https://mlsanity.com/paper/2405.18346/) (2024)
- [Transforming Wearable Data into Personal Health Insights using Large Language Model Agents](https://mlsanity.com/paper/2406.06464/) (2024)
- [History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System](https://mlsanity.com/paper/2302.00907/) (2023)
- [Psychological Counseling Cannot Be Achieved Overnight: Automated Psychological Counseling Through Multi-Session Conversations](https://mlsanity.com/paper/2506.06626/) (2025)
- [The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting](https://mlsanity.com/paper/2402.08658/) (2024)
- [FollowUpBot: An LLM-Based Conversational Robot for Automatic Postoperative Follow-up](https://mlsanity.com/paper/2507.15502/) (2025)

## Built on by (most cited first, 3 of 3)

- [ComBodied Agents: a New Paradigm of Human-Centric Agentic AI](https://mlsanity.com/paper/2608.10915/) (2026)
- [Evaluating Large Language Models in Dynamic Clinical Decision-Making with Standardized Patient Cases](https://mlsanity.com/paper/2606.05112/) (2026)
- [HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices](https://mlsanity.com/paper/2607.21019/) (2026)

## Abstract (arXiv)

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, both within and beyond clinical settings, 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.