- AI-driven Automated Attack Response Strategy Generation System
- Development of Value-Added Resource Conversion Technology for Waste Plastic Pyrolysis Products
- Development of Wearable Neuromorphic Heterogeneous Integration Platform for SPAD-fNIRS-Based Pain Signal Quantification
- Identification of Novel Disease Markers Using Depression Patient-Derived iPSCs
- Development of Sustainable and Highly Functional Polymer Synthesis and Application Technologies for Future Mobility Regulatory Compliance
- AI-driven Discovery of Targeted Protein Degraders — CRBN-based Molecular Glue Degraders
- Ultra-Low-Latency Storage-Driven I/O Subsystem for Large Language Models
- Intelligent E-Skin Foundry Platform
- Korea Sustainable Hybrid Intensification for Fractionation Technology (K-SHIFT)
- kaist_prj10
- ARC-H2: Autonomous Robotics-driven Catalysts for Hydrogen with High Durability
- PFAS-free Research Initiative for Macromolecular Energy materials (PRIME)
- Center for Divertor Science and Innovation in Fusion Energy (D-SINE)
- Quantum teleportation with quantum dot photons of different colors using a system of PIC and ASIC
- Development of AI-Based Super-Gap Core Technology for Next-Generation Eco-Friendly Free-Form Displays
- Net-zero Seawater Refinery; An AI-Based Integrated Refinery Platform for Carbon Capture and Resource Recovery from Seawater
- Transcendent Material Innovation of Phase Transition Artificial Muscles for Soft Robotics
- Development of an AX-based Intelligent Disaster Prevention Platform for Ultra-Safe SMR Construction Against Extreme External Hazards
- nEAR-LINK Initiative: In-Ear Affective BCI-AI Research Network
- Center of AI-BASE (Brain Architecture, Simulation & Engineering)
- Spin-based Neuromorphic/Quantum Hardware Platform
- Green Carbon Capture via AI-assisted Chloroplast DNA Editing
- Digital Health Technologies for Prediction and Intervention in Neurodegenerative Diseases
- Development of AI-Biofoundry Integrated Platform for Rapid On-Site Detection of Polycarbonate Microplastics and BPA Upcycling
Digital Health Technologies for Prediction and Intervention in Neurodegenerative Diseases
KEYWORDKeyword
OBJECTIVE Objective
Our team aims to develop core digital health technologies for the early prediction and personalized intervention of neurodegenerative diseases.
By integrating wearable and at-home monitoring technologies for continuous physiological measurement in daily life, multimodal AI-based diagnosis
and prediction, and non-invasive intervention technologies, we seek to establish a longitudinal digital health platform
that connects monitoring, prediction, intervention, and response evaluation.
Ultimately, this work will contribute to the preventive management of dementia and other neurodegenerative diseases and support the transition toward a future healthcare paradigm.
INTRODUCTION Director's Message
Welcome.
Our research team is developing core closed-loop digital health technologies for the early prediction
and personalized intervention of neurodegenerative diseases.
Neurodegenerative diseases progress gradually over a long period of time, making continuous monitoring
and proactive response in daily life essential.
To address this need, our team aims to establish a digital health platform that integrates wearable & at-home monitoring,
multimodal AI-based prediction, and non-invasive intervention technologies to predict disease status and evaluate intervention outcomes.
Building on the KAIST–KIST collaborative research framework, we will lead future healthcare technologies centered
on prevention, prediction, and intervention for an aging society.
Thank you.
IMPACT Impact
Technological Innovation
Securing core digital health technologies for neurodegenerative diseases
Integrating wearable & at-home monitoring, AI prediction, and non-invasive intervention
Establishing an integrated platform that connects sensing, AI, intervention, and response evaluation
Talent & Research Ecosystem
Building an interdisciplinary research framework through KAIST–KIST collaboration
Strengthening postdoctoral research independence and training next-generation research leaders
Fostering a unique talent ecosystem across digital health, AI, and bioengineering
Social & Industrial Contribution
Reducing healthcare costs through early prediction and preventive management of neurodegenerative diseases
Alleviating caregiving burdens and improving the quality of life for older adults
Creating a solid foundation for emerging industries in wearables, at-home diagnostics, and AI healthcare
TALENT Ideal Talent
Researchers with interdisciplinary expertise across bioengineering, digital health, BioMEMS, and AI, who can integrate sensor development, data analysis, AI-based prediction, clinical validation, and intervention technologies, while demonstrating responsibility and leadership to independently plan and conduct research within the KAIST–KIST collaborative framework