AIMC Topic: Republic of Korea

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The current status of breakthrough devices designation in the United States and innovative medical devices designation in Korea for digital health software.

Expert review of medical devices
INTRODUCTION: Artificial Intelligence (AI) is becoming increasingly utilized in the medical device industry as it can address unmet demands in clinical sites and provide more patient treatment options. This study aims to analyze the FDA's Breakthroug...

A comparative study of regulatory perspectives on innovative medical devices in Korea and the United States.

Expert review of medical devices
INTRODUCTION: As the 4th Industrial Revolution era emerges, medical devices that apply technologies such as big data, artificial intelligence, and 3D printers are on the rise. In April 2019, Korea introduced the Act on Nurturing Medical Devices Indus...

Care robot research and development plan for disability and aged care in Korea: A mixed-methods user participation study.

Assistive technology : the official journal of RESNA
The population of Korea is aging rapidly, and this has led to a care burden for caregivers. Without adequate caregivers to address the increased burden, people with significant disabilities and older adults with disabilities who have greatly reduced ...

Machine learning-based prediction of critical illness in children visiting the emergency department.

PloS one
OBJECTIVES: Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have ...

Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.

Scientific reports
The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be prese...

Asymmetry between right and left fundus images identified using convolutional neural networks.

Scientific reports
We analyzed fundus images to identify whether convolutional neural networks (CNNs) can discriminate between right and left fundus images. We gathered 98,038 fundus photographs from the Gyeongsang National University Changwon Hospital, South Korea, an...

Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Using a LSTM Neural Network Model from Environmental Data.

Toxins
Paralytic shellfish toxins (PSTs) are produced mainly by (formerly ). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a d...

Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices.

Scientific reports
To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the...

Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network.

Sensors (Basel, Switzerland)
Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort a...