Vital Trace: Protocol-Constrained Patient-State Reasoning for Longitudinal Clinical Trajectories
arXiv:2602.12833v2 Announce Type: replace Abstract: Longitudinal clinical reasoning over electronic health records requires tracking evolving physiological measurements, laboratory results, and interventions across extended patient trajectories. Existing LLM-based clinical reasoning systems often rely on repeatedly serializing patient histories or exchanging unconstrained textual agent messages, leading to context drift, unstable reasoning, and growing inference cost over long horizons. We present Vital Trace, a protocol-constrained multi-agent framework for future clinical risk prediction ove
The proliferation of Large Language Models (LLMs) and their integration into complex reasoning tasks, particularly in critical fields like healthcare, is driving the need for more robust and stable architectures at this moment.
This development addresses key limitations of current LLM-based clinical reasoning systems, such as context drift and unstable reasoning over time, which are critical for reliable and scalable AI application in healthcare.
Clinical AI reasoning systems will likely shift towards more structured, protocol-constrained multi-agent frameworks, moving away from unconstrained textual exchanges for improved accuracy and cost-effectiveness.
- · AI healthcare developers
- · Hospitals and clinics adopting AI
- · Patients receiving AI-assisted care
- · Specialized AI framework providers
- · Developers of unstable LLM-based healthcare systems
- · Systems heavily reliant on unconstrained textual agent messages
- · Legacy clinical decision support systems
Improved reliability and cost-efficiency of AI-driven patient care and risk prediction.
Accelerated adoption of AI in longitudinal healthcare management, leading to better patient outcomes and resource allocation.
The establishment of new regulatory and ethical guidelines specifically for protocol-constrained multi-agent AI in critical applications like medicine, setting precedents for other sectors.
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