Clinical Evidence-based AI & Data Science Lab

Principal Investigator: Chin Lin, PhD

Present
2026/02–Present Professor, School of Medicine, National Defense Medical University
2026/02–Present Director, Medical Technology Education Center, National Defense Medical University
2024/06–Present Director, AI Core Lab, Military Digital Medical Center, Tri-Service General Hospital

Experience
2021/08–2026/01 Associate Professor, School of Medicine, National Defense Medical University
2019/07–2026/01 Deputy Director, Medical Technology Education Center, National Defense Medical University
2019/06–2024/06 Chief Technology Officer, AIoT Center, Tri-Service General Hospital
2020/07–2024/05 Deputy Secretary-General, Aviation Medical Association R.O.C.
2018/08–2021/07 Assistant Professor, Graduate Institute of Life Sciences, National Defense Medical University
2017/07–2018/07 Postdoctoral Research Fellow, Department of Research and Development, National Defense Medical University
2016/08–2018/07 Adjunct Assistant Professor, School of Public Health, National Defense Medical University

Academic Service
2025/01–Present  Associate Editor, npj Digital Medicine
2022/05–2024/12 Editorial Board Member, Scientific Reports
2021/11–2023/07 Guest Editor, Journal of Personalized Medicine

Dr. Chin Lin is a data scientist whose work centers on establishing clinical evidence standards for medical artificial intelligence. His research integrates large-scale electronic health records, electrocardiography, medical imaging, and home-based sensing to develop AI systems that are not only accurate, but clinically actionable and outcome-improving.
He has led or co-led more than ten pragmatic randomized controlled trials evaluating AI-enabled clinical workflows, with results published in Nature Medicine, NEJM AI, Radiology, and Nature Communications. His team developed an AI-ECG platform that has received TFDA approval and USFDA Breakthrough Device Designation, has been transferred to industry, and deployed across multiple hospitals and rural screening programs.
A central theme of his work is treating AI not as a standalone model, but as a system-level intervention that links prediction, clinical action, and patient outcomes within real-world healthcare environments.

Selected publications

1. Lin CS, Liu WT, Tsai DJ, Lou YS, Chang CH, Lee CC, Fang WH, Wang CC, Chen YY, Lin WS, Cheng CC, Lee CC, Wang CC, Tsai CS, Lin SH, Lin C* (2024, May). AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial. Nature Medicine, 30(5):1461–1470.
2. Lin C, Liu WT, Chang CH, Lee CC, Hsing SC, Fang WH, Tsai DJ, Chen KC, Lee CH, Cheng CC, Hung YJ, Lin SH, Tsai CS, Lin CS* (2024, Jun). Artificial Intelligence–Powered Rapid Identification of ST-Elevation Myocardial Infarction via Electrocardiogram (ARISE) — A Pragmatic Randomized Controlled Trial. NEJM AI, 1(7):AIoa2400190.
3. Lin C, Tsai DJ, Wang CC, Chao YP, Huang JW, Lin CS, Fang WH* (2024, Jun). Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial. Radiology, 311(3):e231937.
4. Lin C, Lin CS, Chen SJ, Tsai SH, Sung CC, Chen CC, Hsu YJ, Hsu YJ, Lin SH* (2026, Jan). AI-enabled Electrocardiogram Alert for Potassium Imbalance Treatment: A Pragmatic Randomized Controlled Trial. Nature Communications, 17:159.
5. Lin C, Chau T, Lin CS, Shang HS, Fang WH, Lee DJ, Lee CC, Tsai SH, Wang CC, Lin SH* (2022, Jan). Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction. npj Digital Medicine, 5(1):8.
6. Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, …, Lin C, …, Flores MG*, Li Q (2021, Oct). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27(10):1735–1743.