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@@ -187,11 +187,32 @@ Advanced Machine Learning Method Development
Medical & Clinical AI: Transforming Healthcare with AI
One of the ambitious goals of AI lies in the field of medicine. HAIL is committed to applying advanced ML technologies to healthcare in order to improve the quality of medical services and expand access to care for a greater number of people. Our work spans several cutting-edge projects that harness AI to improve patient care and clinical decision-making.
- - Development of Liver Disease Support Software (with Dr. Answer 2.0 Liver Disease Team, Ajou University Hospital): Electronic medical records (EMR) serve as a hub of clinical data that captures a wealth of information about patient events. These records are a treasure trove for running intelligent medical systems. At HAIL, we are processing EMR data to enable machine learning applications, which help develop various clinical decision support solutions related to liver diseases.
- - AI Algorithm for Human-Derived Specimen Image Recognition (with HEM Pharma): Human-derived specimens can provide valuable insights into a person's health status. In this project, we are developing AI algorithms that automatically process images of human specimens, for continuous health monitoring. Our solution automates the generation and comparison of machine learning models to ensure the use of up-to-date recognition models, along with continuously collected data. This approach ensures timely maintenance of the machine learning system that provides accurate health assessments.
- - Risk Model and Analysis for Pancreatic Cancer (with Herings): In South Korea, the National Health Insurance program mandates biennial health checkups for all citizens. Using health checkup data combined with follow-up records from the National Cancer Center on pancreatic cancer diagnoses, we have built predictive models that assess an individual's risk of developing pancreatic cancer. Our risk analysis offers the potential to identify high-risk individuals early, which in turn leads to better prevention and treatment strategies.
- - Multi-Modal Healthcare Method Development (with Prof. Yuyin Zhou, UCSC): One of the next milestones in machine learning is the ability to blend data from multiple modalities to maximize available information and refine predictive models. Starting with the RadFusion dataset from the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), this project explores how to integrate various data modalities generated in healthcare settings, such as EMRs, imaging data, drug structures, free-text notes, and medical ontologies, into robust decision-making models. By doing so, we aim to improve the accuracy of clinical predictions and support optimal healthcare decisions.
+ - Development of Liver Disease Support Software (with Dr. Answer 2.0 Liver Disease Team, Ajou University Hospital): Electronic medical records (EMR) serve as a hub of clinical data that captures a wealth of information about patient events. These records are a treasure trove for running intelligent medical systems. At HAIL, we are processing EMR data to enable machine learning applications, which help develop various clinical decision support solutions related to liver diseases.
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+ - AI Algorithm for Human-Derived Specimen Image Recognition (with HEM Pharma): Human-derived specimens can provide valuable insights into a person's health status. In this project, we are developing AI algorithms that automatically process images of human specimens, for continuous health monitoring. Our solution automates the generation and comparison of machine learning models to ensure the use of up-to-date recognition models, along with continuously collected data. This approach ensures timely maintenance of the machine learning system that provides accurate health assessments.
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+ - Risk Model and Analysis for Pancreatic Cancer (with Herings): In South Korea, the National Health Insurance program mandates biennial health checkups for all citizens. Using health checkup data combined with follow-up records from the National Cancer Center on pancreatic cancer diagnoses, we have built predictive models that assess an individual's risk of developing pancreatic cancer. Our risk analysis offers the potential to identify high-risk individuals early, which in turn leads to better prevention and treatment strategies.
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+ - Multi-Modal Healthcare Method Development (with Prof. Yuyin Zhou, UCSC): One of the next milestones in machine learning is the ability to blend data from multiple modalities to maximize available information and refine predictive models. Starting with the RadFusion dataset from the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), this project explores how to integrate various data modalities generated in healthcare settings, such as EMRs, imaging data, drug structures, free-text notes, and medical ontologies, into robust decision-making models. By doing so, we aim to improve the accuracy of clinical predictions and support optimal healthcare decisions.
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@@ -221,7 +242,7 @@ Anomalies in Surveillance Video
Dangerous Object Detection and Tracking using YOLO: While anomaly detection in surveillance video is effective for identifying unusual behaviors such as violence, vandalism, abduction, or trespassing, etc., it may not always trigger alerts based solely on the presence of dangerous objects. However, the appearance of weapons, firearms, or other objects that could be used in crimes serves as a crucial indicator of potential danger. This project focuses on fine-tuning the well-known YOLO object detector to identify dangerous objects. This would help enhance surveillance capabilities by identifying potential threats at an earlier stage.
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Anomalies in Surveillance Video
Video Indexing for Rapid Surveillance Footage Summarization (VIDEX, with GMDSOFT): Alongside anomaly detection and object detection methods, we are developing applications to make the analysis of surveillance footage more efficient. This project involves creating a video indexing system that allows the results of anomaly and object detection to be stored, searched, and navigated easily. The goal is to assist investigators in maximizing their efficiency when analyzing large volumes of video data by providing quick access to critical segments.
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