Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.

A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires specialized knowledge. In addition, inappropriate annotation processes can increase model bias.

Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks.

One of the features of artificial intelligence/machine learning-based medical devices resides in their ability to learn from real-world data. However, obtaining a large number of training data in the early phase is difficult, and the device performance may change after their first introduction into the market. To introduce the safety and effectiveness of these devices into the market in a timely manner, an appropriate post-market performance change plan must be established at the timing of the premarket approval.

Regulatory convergence of medical devices: a case study using ISO and IEC standards.

Introduction: Achieving regulatory convergence is important in providing safe and effective medical devices to patients in a timely manner. The use of standards set by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), may be an important tool for regulatory convergence. The International Medical Device Regulators Forum (IMDRF) published a survey and statements regarding the use of these standards in each IMDRF jurisdiction, which showed that each jurisdiction proactively uses these standards in its regulation.