Transforming Healthcare with AI and State-of-the-Art Natural Language Processing

Academia continues to advance the field of Artificial Intelligence. However, while published research results in deep learning, transfer learning, computer vision, and natural language processing have surfaced a vast number of use cases, the ability to integrate these techniques in real-world projects- to improve medical diagnosis, treatment, and prognosis across the entire healthcare value chain, is in the very early stages. Challenges range from AI issues like model drift, bias, locality, and explainability, to data issues like privacy, compliance, scalability, and quality, all the way to changing the behavior of physicians and patients to take advantage of new capabilities. This session will explore the most current lessons learned, best practices, and known risks in applying state-of-the-art NLP & AI techniques in real-world healthcare projects. What attendees will learn: • What new capabilities and potential benefits do new advances in NLP & AI enable? • What are the most common obstacles and mistakes that teams make in trying to apply these capabilities? • What are known best practices and lessons learned that you should apply in your next project? Speakers: David Talby, PhD - CTO at John Snow Labs Esteban Rubens - Principal, Healthcare AI at NetApp Andrew Malinow, PhD - AI Practice Lead/Data Scientist at ePlus