The SLK Research Institute conducts applied research at the intersection of emergency medicine and AI, translating clinical insight into technology that works where it matters most.
The Project SLK Research Institute for AI Medical Innovation is the dedicated research arm of Project SLK Ltd., focused on the systematic study of AI applications in clinical medicine.
Grounded in real-world emergency department data and frontline clinical experience, our work spans post-cardiac arrest syndrome (PCAS) prognostication, automated ED triage and severity classification, and AI-driven clinical decision support systems — all designed to be practical, validated, and deployable.
Our findings are published in SCI/SCIE-indexed international journals and directly inform Project SLK's product pipeline, including the Compact Emergency Unit (CEU), creating a virtuous cycle between rigorous research and real-world implementation.
"For AI to truly serve underserved communities, it must be built not as a laboratory model but as a tool that functions in the field."— From the Institute's founding statement
Developing multimodal AI models for neurological outcome prediction after cardiac arrest, leveraging the multicenter KORHN registry with temporal clinical features, biomarkers, and neurophysiological data.
Building prediction models from 35,000+ ED encounters for automated severity classification, undertriage detection, and adverse outcome prediction in discharged patients, with emphasis on explainability.
Conducting the technical design, validation, and health-economic modeling for AI-powered compact emergency care units deployable in medically underserved regions.
Access to the Catholic Medical Center (CMC) Clinical Data Warehouse spanning 8 hospitals, alongside the multicenter KORHN cardiac arrest registry — providing large-scale, real-world clinical datasets.
Seoul St. Mary's Hospital Department of Emergency Medicine, Korean Hypothermia Network (KORHN), and the College of Medicine at The Catholic University of Korea.
Python & R-based ML pipelines, XGBoost / LightGBM / deep learning architectures, and Explainable AI (SHAP) frameworks for transparent, clinically interpretable models.
We welcome opportunities in collaborative clinical AI research, data analytics partnerships, and industry–academia engagement.
research@projectslk.com