Agentic Adaptive AI for Clinical Care: Autonomous Construction of Patient-Specific Models

We present an adaptive framework for personalized medicine designed to discover and deploy predictive models tailored to specific patients and their unique physiological dynamics. While static population models fail to capture individual physiological heterogeneity, standard patient-specific fine-tuning is not uniformly beneficial; sparse calibration budgets, high-frequency sensor noise, and severe domain shifts frequently degrade model accuracy below the original baseline, yet conventional systems lack mechanisms to assess whether adaptation should occur or when an unreliable model must be rejected. To address this, our architecture pairs an autonomous AutoResearch agent that iteratively searches across adaptation spaces (architectures, adapter ranks, and loss schedules) with a deterministic verification gate and a domain-specialized diagnostic agent. When a candidate breaches stability or physical invariants, the system triggers a diagnostic recovery loop, providing clinical oversight to pinpoint failure modes and safely arbitrate between matched cohort retrieval, training a scratch patient-only baseline, or refusing inference via data recollection. We evaluate our framework across two distinct clinical modalities: continuous ICU arterial blood pressure tracking from ECG and PPG waveforms (144 unseen PulseDB subjects), and simulation-to-real bioimpedance monitoring for dynamic fluid-shift tracking. Most notably, across both tasks, the framework cuts prediction error by approximately 50% relative to baseline model.