Personalized LLM-generated plain language summaries improve lay readers' comprehension and quality ratings but increase risks of reinforcing biases and introducing hallucinations compared to static expert summaries.
Health-llm: Large language models for health prediction via wearable sensor data
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VitalAgent adds longitudinal memory and tool-augmented reasoning to an agent for reactive QA and proactive monitoring on ECG/PPG streams, reporting >25% gains over baselines on a new 1,862-pair + 90-hour benchmark.
TimeSRL uses semantic abstractions from time-series data optimized via reinforcement learning to achieve better cross-dataset generalization than standard ML or LLM baselines in mental health prediction.
A cross-modal masked autoencoder creates reusable biosignal fingerprints that match or exceed specialist models on seven cardiovascular tasks using only single-modality input.
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
SparrowSNN introduces SSF activation, a tunable hybrid ANN-SNN, and reconfigurable ASIC achieving SOTA accuracy on MIT-BIH ECG with 20-100x lower energy than prior ultra-low-power solutions.
LLMs accelerate research workflows from idea generation to writing but introduce challenges like hallucination, bias, opacity, and ten systemic risks requiring new governance frameworks.
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.
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VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data
VitalAgent adds longitudinal memory and tool-augmented reasoning to an agent for reactive QA and proactive monitoring on ECG/PPG streams, reporting >25% gains over baselines on a new 1,862-pair + 90-hour benchmark.