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mHealthLAB pioneers a new paradigm in intelligent healthcare, seamlessly integrating innovative sample pretreatment based on sensor and nano-technology with advanced artificial intelligence (AI) diagnostic algorithms. From smartphone-based point-of-care testing (POCT) and Physiological AI leveraging organoids and bio-signals, to Physical AI integrating advanced instruments like AFM and Raman spectroscopy, we are leading the future of predictive and precision medicine.
고려대학교 mHealthLAB(이정훈 교수 연구실) 방문을 환영합니다.
본 연구실은 나노바이오 전처리 기술/센싱기술과 인공지능(AI)을 융합하여 현장진단(POCT)의 민감도와 정확도를 혁신하는 연구실니다. BEETLES² 및 SaliFilter 등 독자적 샘플 전처리 플랫폼으로 검출한계를 극복하였고, SMARTAI-LFA와 TIMESAVER 딥러닝 알고리즘을 통해 스마트폰만으로 2분 내 전문가 수준의 진단을 구현하였습니다. 최근에는 라만분광·AFM 등 계측장비를 자율 제어하는 Physical AI, 오가노이드·생체신호·디지털 에이징 기반의 Physiological AI로 연구 영역을 확장하고 있습니다. 센서/진단/인공지능 헬스케어를 연구키워드로 삼고 있으며, 연구를 위한 연구가 아닌 실제 인간을 이롭게 하는 연구에 큰 관심이 있습니다.
We redefine the 'm' in our name to represent a holistic approach: utilizing Machine-intelligent systems, integrating diverse Multi-modal datasets, and probing biological realities at the Micro/nano-scale.
Our mission is to build an intelligent, connected healthcare ecosystem by converging advanced AI, precision medicine, and cutting-edge sensing technologies. We translate complex biological and clinical data into real-time, actionable health insights.
Machine-intelligent Platforms We develop robust AI systems tailored for biomedical applications, utilizing deep generative models, transformer architectures, and multi-agent frameworks. By leveraging edge AI and federated learning, we ensure our solutions are privacy-preserving, low-power, and ready for practical field deployment.
Multi-modal Data Fusion Our research integrates diverse data streams—combining mobile signals, medical imaging, spectroscopic data, and clinical records—through advanced multimodal learning. This fusion enables rapid, intelligent diagnostics and deeply personalized patient monitoring.
Micro/nano-scale Precision We pioneer innovations at the intersection of bio and engineering, from nanofilter-based sample preprocessing and on-site molecular diagnostics to MEMS, SERS/Raman smart sensors, and AFM-based nanoscale characterization. These technologies allow for precise, real-time biosignal interpretation.