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Charmgil Hong authored and Charmgil Hong committed Sep 17, 2024
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Expand Up @@ -188,6 +188,23 @@ <h2>Medical & Clinical AI: Transforming Healthcare with AI</h2>
<p>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.</p>
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<li><strong>Development of Liver Disease Support Software (with Dr. Answer 2.0 Liver Disease Team, Ajou University Hospital)</strong>: 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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<li><strong>AI Algorithm for Human-Derived Specimen Image Recognition (with HEM Pharma)</strong>: 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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Expand All @@ -196,23 +213,6 @@ <h2>Medical & Clinical AI: Transforming Healthcare with AI</h2>
<li><strong>Multi-Modal Healthcare Method Development (with Prof. Yuyin Zhou, UCSC)</strong>: 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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