Conversational Artificial Intelligence for Real-Time, Personalized Health Monitoring

A conversational large language model (LLM) that analyzes health data and communicates with patients to provide actionable insights.
Problem:
Wearable devices and implantable sensors generate a deluge of continuous data, but clinicians and patients lack efficient tools to translate this information into timely, actionable insights. Existing monitoring systems rely on passive data collection and simple alerts, offering limited personalization or real-time guidance. Artificial intelligence (AI) has the potential to transform chronic disease management, particularly for conditions such as epilepsy requiring continuous monitoring. However, direct patient-LLM communication is limited by data privacy concerns, response reliability, and complex approval pathways in health systems.
Solution:
This invention integrates real-time health data with AI-driven analysis and provides a personal health monitoring experience through a conversational LLM. This system proactively generates context-aware insights and makes complex physiological data more accessible and understandable. This invention can be impactful for chronic conditions, such as epilepsy, where continuous, integrated physiological and behavioral monitoring are essential.
Technology:
Real-time electroencephalogram (EEG) signals are streamed to a secure cloud environment for analysis. There, AI models extract clinically relevant biomarkers, including seizure events, sleep patterns, and neural activity. In parallel, a web or mobile application enables bidirectional interaction through a conversational LLM centered on the patient’s own health data. The system initiates context-aware messages following significant physiological events and supports patient-initiated queries. Safety filters evaluate all outputs, and patient annotations and interaction history iteratively refine model performance and personalization.
Advantages:

Two-way dialogue between a patient and their own physiological data in real time to make health data more actionable.
Bridge objective physiological measurements and patient-reported experiences.
Deployed in the clinical environment with epilepsy patients, successfully processing 80+ days of continuous EEG data and recording 1300+ patient-LLM messages.

Stage of Development:

Proof of Concept

Real-time patient electroencephalogram (EEG) signals are automatically processed and analyzed in the cloud. The conversational LLM alerts patients of time-sensitive health events and answers questions through a chat-based user interface.
Intellectual Property: