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Revolutionizing individual health through personal models and perpetual guidance.
IFH aims to integrate lifestyle, community, environment, and social factors in conjunction with clinical knowledge to radically transform health systems away from hospitals and into the hands of each individual.
Revolutionizing individual health through personal models and perpetual guidance.
Forming partnerships with researchers, practitioners, and organizations across the globe to address the diverse geo-social-economic-cultural factors of health research and guidance.
Understanding real life behavior of individuals and populations by collecting large volumes of data and selecting appropriate sets using right control parameters.
📢 New Dataset Release: Loneliness & Well-Being in Immigrants
We are excited to announce that our dataset — “Loneliness and well-being in Finnish immigrants: A multimodal dataset from wearables and passive data collection” — is now publicly available via Dryad.
openCHA – Stress Estimation and Recommendation Agent
The openCHA framework allows LLMs to connect with external data sources, enabling the collection of information and the creation of customized responses.
Recognize AI as Augmented Intelligence
AI is a powerful force in technology, offering both optimism and concern. It should be seen as a partner, not a replacement for human intellect, augmenting our capabilities responsibly to maximize benefits and mitigate risks.
Artificial intelligence predicts who will develop dementia in two years
AI can predict if people develop dementia within two years. The prediction accuracy is 92 percent, obtained in a largescale study conducted on more than 15,400 patients in the U.S.
Scaling Trusted, High-Impact AI Care Navigation
IFH & Digital Medicine Society (DiMe)January 9, 2026, at 8:00 a.m. ET , 5:00 a.m PT
Online | Global Initiative
Advancing Global Collaborations in Mental Health and Digital Innovation
, at 11:00 AM – 1:00 PM
Susan & Henry Samueli College of Health Sciences, UC Irvine — Room 4100
Tabetha Harken
Julie Rousseau
- All
- Future Healthcare
- Lifestyle Recommendation
- Large Language Models
- Personal Health Models
- Population Models
- Translation and Practice
- Health State Estimation
Clinical Trial Adherence: Intelligent Systems for Monitoring and Support
This project develops intelligent systems to improve patient adherence in clinical trials, addressing one of the major causes of trial…
Clinical Trial Enrichment: Intelligent Agent Systems for Patient–Trial Matching
This project develops intelligent agent systems that match patients to clinical trials by integrating EHR data with trial eligibility criteria.…
Reducing Interdataset Covariate Shift in Sleep EEG of Traumatic Brain Injury Using Transfer Euclidean Alignment
This project introduces Transfer Euclidean Alignment (TEA), a transfer learning technique that reduces variability across sleep EEG datasets to improve…
Domain-Specific Constitutional AI: Enhancing Safety in LLM-Powered Mental Health Chatbots
This project advances the safety of mental health chatbots by adapting Constitutional AI (CAI) with domain-specific principles tailored to psychological…
MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering
This project develops MedCoT-RAG, a domain-specific framework that enhances medical question answering by combining causal-aware retrieval with structured chain-of-thought prompting.…
Linkage Attacks Expose Identity Risks in Public ECG Data Sharing
This project investigates privacy threats in publicly shared electrocardiogram (ECG) data, where biometric features make individuals vulnerable to re-identification. Unlike…
Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Data
This project introduces a framework that uses wearable sensor data to generate personalized counterfactuals — answering “what if” questions about…
FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation
This project develops FairTabGen, a fairness-aware LLM-based framework for generating synthetic tabular data. By unifying counterfactual and causal fairness definitions…
REACT: Reinforcement Learning-Based Adaptive ECG Anonymization and Privacy Threat Mitigation
This project introduces REACT, a reinforcement learning framework that protects sensitive electrocardiogram (ECG) data against re-identification threats. By dynamically injecting…
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