Welcome to Biomedical

Intelligence Laboratory

Biomedical Intelligence Laboratory (BiLab)’s mission is to research and develop technologies that empower clinicians and ultimately enhance quality of life. Our work encompasses not only AI-driven radiological applications but also multi-modal biomedical data analysis leveraging advanced AI techniques.

BiLab's research areas are:

(1) AI-driven Radiology Applications

(2) Multi-modal Biomedical Data Analysis

(3) Advanced AI Techniques for Medical Applications

Research Areas

Medical Image,

Biosignal,

Lifelog Data,

Genomic Data,

AR assisted Surgery,

VR Medical Simulation & Training

Medical Image Analysis - AI in Healthcare

This area leverages AI to perform in-depth analyses of radiological images covering the entire human body, including modalities such as X-ray, CT, MRI, and Ultrasound. Our objective is to demonstrate the clinical utility of AI in radiology applications and ultimately enable radiologists to integrate these solutions effectively into real-world clinical settings.

Biosignal Analysis - AI in Healthcare

This area comprehensively analyzes diverse multi-modal biomedical data—including endoscopic images, bio-signals, omics data, and EHR—to offer in-depth insights into disease mechanisms and facilitate personalized medicine. Our objective is to leverage hospital real-world data (RWD) to expand the applications of a Large Multi-modal Model (LMM) and Agentic AI, and to validate them in clinical settings.

Lifelog Data Analysis - AI in Healthcare

This area is dedicated to exploring innovative AI methods specifically designed for medical applications. We focus on developing novel algorithms and optimization strategies to address a wide range of challenges in the clinical domain. Our objective goes beyond methodological advancement; we aim to create AI solutions that seamlessly integrate with clinical workflows and settings, ultimately enhancing patient care.

Genomic Data Analysis - AI in Healthcare

Lifelog data is not measured by medical institutions, but includes exercise and healthcare data measured with a mobile phone or IoT sensor during daily life. We analyze lifelog data considering the characteristics by applying machine learning, and deep learning technologies.

AR assisted Surgery - XR in Healthcare

Electronic Health Records(EHR) contain key patient information and can be shared in different healthcare environments. We support clinicians who want to utilize EHR-Common Data Model(CDM), and perform multimodal data research that connects to unstructured data and EHR-CDM.

VR Medical Simulation & Training - XR in Healthcare

The Augmented Reality(AR) technology is utilized to assist the surgery by registration of patient's anatomical structures. We research the segmentation method for 3D anatomical structures from patient images before surgery, and registration method for overlaying on the target position during surgery.

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