Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
arXiv:2606.02812v1 Announce Type: cross Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based multi-agent systems address context length but process patients in isolation, failing to mirror how clinicians leverage accumulated experience from similar prior cases. We present Traj-Evolve, a self-evolving multi-agent system with two complementary evolving mechanisms. First, an Experience Pool (ExPool) acts as a non-parametric memory, indexing rejection-sampled reaso
The paper leverages the rapid advancements in large language models and multi-agent systems, combined with growing demands for personalized and intelligent healthcare solutions.
This development indicates a significant step towards autonomous AI systems that can learn and adapt from collective experience, potentially transforming clinical decision-making and patient care.
Traditional patient modeling, which often processes individuals in isolation, will be augmented or replaced by systems capable of leveraging accumulated experience from similar cases.
- · AI healthcare providers
- · Oncology researchers
- · Patients with complex diseases
- · AI agent developers
- · Legacy EHR systems
- · Traditional clinical decision support systems
Improved early detection rates and personalized treatment plans for lung cancer patients.
Accelerated development of similar 'self-evolving' AI agent systems across various medical and non-medical domains.
Ethical and regulatory debates around the autonomous decision-making capabilities of AI agents in critical sectors like healthcare.
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Read at arXiv cs.CL