Stanford CS547 HCI Seminar | Spring 2026 | HCI and Human-Centered AI for Digital Health
TL;DR
This seminar explores how personalized foundation models trained on wearable data can predict intervenable health events (like cravings or blood pressure spikes) to enable just-in-time digital interventions, but emphasizes that HCI is critical to solve adherence challenges, inconsistent labeling, and misaligned evaluation metrics.
🧠 Personalized AI Architecture 3 insights
Self-supervised learning on biosignals
Models predict missing portions of bio-signals from surrounding data, creating personalized foundation models per user without requiring extensive labeled datasets.
One model per person paradigm
Unlike traditional diagnostic AI using a single general model, this approach trains separate models on individual longitudinal data to predict intervenable events like substance cravings or blood pressure spikes.
Multimodal physiological relationships
Pre-training learns how signals like heart rate variability and accelerometry relate to each other within an individual's specific physiology to enable downstream predictions.
⚠️ Real-World Deployment Failures 3 insights
Nurse study adherence breakdown
During COVID-19, nurses showed inconsistent wearable adherence and highly variable stress labeling patterns both across individuals and within the same person over time, rendering personalized models ineffective.
AI's behavioral limitation
Personalization fails when users provide inconsistent ground-truth labels, proving that algorithmic innovation alone cannot overcome fundamental human behavioral complexities.
Actionable over precise predictions
Clinical utility comes from detecting intervenable events like blood pressure spikes rather than predicting exact numerical values that would not change the clinical action taken.
🎯 HCI Solutions for Burden and Evaluation 3 insights
Intelligent burden reduction strategies
Combining active learning with passive sensing detects optimal moments for user queries while automatically avoiding interruptions during activities like driving or school pickup.
Prevalence-sensitive metric awareness
Standard metrics like precision shift dramatically with population prevalence, while sensitivity and specificity provide stable evaluation across different clinical deployment contexts.
Causal adherence modeling
Intervention timing and content create causal chains affecting user burden, receptivity, and adherence, requiring HCI study designs that measure human behavior beyond algorithmic accuracy.
Bottom Line
Effective digital health AI requires pairing personalized foundation models with HCI methods that explicitly address adherence challenges, ensure consistent human labeling, and employ clinically relevant evaluation metrics to deliver interventions at the right moment without excessive user burden.
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