Neural networks have been widely adopted across many industries as the ultimate pattern recognition tool. While their current uses in healthcare are limited, neural networks have a promising future in diagnostic and decision making applications, because of their ability to mimic—and improve on—human capabilities in health-related advice and treatment. This video explains the basics of neural networks; shows examples of training neural networks with both image-based and unstructured healthcare data; and describes the kinds of neural networks most likely to be useful for health-related applications. Examples are introduced using Python. Learners should have a basic understanding of Python, statistics, and machine learning (i.e., how training works and how to prepare data for training a model).

• Survey a range of current neural network applications in healthcare using Python and TensorFlow
• See examples of neural networks that underperform or get tricked in the health domain
• Explore the promising future of using neural networks for pattern recognition problems in healthcare

Aileen Nielsen is a software engineer at One Drop, an AI/ML healthtech company working on diabetes management products. A member of the New York City Bar Association’s Science and Law committee, Aileen holds degrees in anthropology, law, and physics from Princeton, Yale, and Columbia, respectively. She focuses on improving daily life for underserved populations—particularly groups who have yet to fully enjoy the benefits of mobile technology.