FDA Artificial Intelligence and Machine Learning Fellowship

US Food And Drug Administration – FDA Artificial Intelligence and Machine Learning Fellowship Company Name US Food And Drug Administration Position Title FDA Artificial Intelligence and Machine Learning Fellowship Company Information

Food and Drug Administration (FDA), Center for Devices and Radiological Health (CDRH) located in White Oak, Maryland.

Oak Ridge Institute for Science and Education (ORISE) Research Participation Programs at the U.S. Food and Drug Administration are educational training programs designed to provide students and recent graduates, opportunities to participate in project-specific research and developmental at the Center for Devices and Radiological Health (CDRH). The mission of CDRH is to protect and promote public health. CDRH assures that patients and providers have timely and continued access to safe, effective, and high-quality medical devices and safe radiation-emitting products.

CDRH provides consumers, patients, caregivers, and providers with understandable and accessible science-based information about products. CDRH facilitates medical device innovation by advancing regulatory science, providing industry with predictable, consistent, transparent, and efficient regulatory pathways, and assuring consumer confidence in devices marketed in the U.S.

Research Project:
The Artificial Intelligence Regulatory Science Program conducts regulatory science research ensuring safe and effective AI/ML-enabled medical devices across rapidly expanding healthcare applications including image processing, disease detection, diagnosis, and therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data.

Major regulatory science gaps include lack of methods for AI algorithm training with limited data, bias analysis and minimization, performance metrics and uncertainty quantification, evaluation of continuously learning algorithms, and post-market monitoring.

Learning Objectives:
Under the guidance of an FDA mentor, you will:

• Gain comprehensive understanding of regulatory science approaches for AI/ML-enabled medical devices, including performance assessment methodologies and uncertainty quantification methods.
• Acquire specialized skills in addressing critical data challenges in medical AI, including methods for training algorithms with limited datasets, synthetic data evaluation, and approaches to minimize algorithmic bias.
• Develop expertise in evaluating AI devices that can adapt and learn post-deployment, including understanding evolving algorithms and creating methodologies to assess algorithm performance changes over time.

• Recent Ph.D. degree in mathematics, statistics, physics, or related field
• Proficiency in at least one programming language (Python, R, Matlab, etc)
• Intelligence
• Interest in research