International Center for Health Information Technology
Research Field
Dr. Chih-Wei (Grace) Huang received her Ph.D. in Medical Informatics from Taipei Medical University in 2016 and is currently a Research Fellow at the International Research Center for Health Information Technology, Taipei Medical University. She has published over 60 SCI/SSCI/EI journal articles and participated in more than 40 funded research and industry–academia collaboration projects, serving as Principal Investigator or Co-Principal Investigator in multiple national and international programs. Her expertise includes artificial intelligence in healthcare, clinical data science, digital health interventions, and real-world evidence. She has been involved in over 10 patent applications, with multiple granted patents related to AI-enabled digital health technologies. Since 2020, she has served as a startup consultant for the TMU SPARK program and founded the Digital Health Translation (DHT) initiative, later expanded into the Digital Health Translation Alliance (DHTA). Her current research focuses on AI-enabled digital health applications for chronic kidney disease and dialysis populations, supported by National Science and Technology Council–funded projects.
The International Center for Health Information Technology (ICHIT) advances AI-enabled digital health innovations with a strong emphasis on international collaboration, clinical translation, and real-world implementation. Working closely with Taipei Medical University’s affiliated hospitals and global partners, ICHIT bridges research, clinical practice, and healthcare innovation.
The center integrates artificial intelligence and data-driven methods into healthcare applications, including clinical decision support, real-world evidence analytics, multimodal risk prediction, and digital health interventions. Leveraging electronic health records, wearable devices, and multimodal clinical data, ICHIT develops scalable and clinically validated AI solutions. Through the Digital Health Translation (DHT) initiative and the Digital Health Translation Alliance (DHTA), the center actively promotes industry–academia collaboration, clinical validation, and pathways to commercialization.
The research of ICHIT focuses on the development and clinical translation of AI-enabled digital health solutions, with emphasis on chronic kidney disease (CKD) and dialysis populations. Core themes include digital health care for CKD, applying healthcare technologies to improve disease management, patient engagement, and clinical decision-making. The center advances big data analytics and disease trajectory modeling using large-scale longitudinal data, time-series analysis, and visualization to support early risk identification and precision prevention. ICHIT also develops machine learning applications for comorbidity prevention, drug dosage optimization, personalized health education, and wearable-based digital interventions, as well as multimodal fracture risk prediction models integrating clinical and wearable data for real-world validation.
- 2020 Taipei Medical University Student-Faculty Joint Academic Research Presentation - Honorable Mention (for supervising students presenting research outcomes of the Ministry of Science and Technology Undergraduate Project) [2022]
- Award Winner in the 19th National Innovation Award - New Venture Group, for the project "PROPHET - Developing Lung Cancer Digital Screening Using Data Science" [2022]
- Selected as a TWCC Elite Startup by the National High-Speed Network's TWCC StarCraft - The PROPHET team from Taipei Medical University was awarded the highest honor, Level 3 New Venture Team, receiving the prestigious National High-Speed Network award of 34,560 GPU hours of computing resources [2021]
- 2016 PhD, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
- 2012 MSc, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
- 2010 BBA, School of Healthcare Administration, Taipei Medical University, Taipei, Taiwan
Job Description
- Analyze digital health projects from clinical, business, and policy perspectives
- Support market analysis, value proposition design, and use-case definition for AI and digital health solutions
- Assist in cost-effectiveness, cost–benefit, or reimbursement-related analyses
- Translate research outcomes into business-relevant insights (e.g., service models, deployment scenarios)
- Collaborate with interdisciplinary teams including clinicians, data scientists, and industry partners
Preferred Intern Educational Level
- Senior undergraduate students (Year 3 or above)
- Master’s students
- PhD students
Skill sets or Qualities
- Background or strong interest in healthcare management, health economics, information management, or public health
- Ability to explore problems, structure questions, and propose solutions independently
- Strong analytical thinking and communication skills
- Interest in digital health, AI in healthcare, and real-world implementation
- Experience with data analysis or healthcare systems is a plus
Job Description
- Process and analyze medical or health-related image data
- Develop and evaluate AI / machine learning models for imaging-based tasks
- Participate in problem formulation, feature exploration, and model improvement
- Collaborate with clinicians and researchers to align AI models with clinical needs
- Support research documentation, experiments, and result interpretation
Preferred Intern Educational Level
- Senior undergraduate students (Year 3 or above)
- Master’s students
- PhD students
Skill sets or Qualities
- Background in computer science, biomedical engineering, medical informatics, or related fields
- Experience or strong interest in image processing, machine learning, or deep learning
- Ability to identify problems, explore data, and iterate solutions
- Hands-on, curious, and comfortable working with imperfect real-world data
- Experience with Python, PyTorch/TensorFlow, or image analysis tools is a plus