AI Health Spark Seminar Series: A Transparency- and Trust-Centric Design Approach to AI for Healthcare
Artificial Intelligence (AI), particularly thanks to the advances of machine learning (ML) and deep learning (DL) in recent years, holds tremendous promise and potential for enhancing clinical decision support in healthcare. From disease diagnosis to prognosis to treatment planning to patient triaging, AI-driven clinical decision support across the entire clinical workflow can great improve the accuracy, consistency, and speed with which clinicians can better serve their patients for greater quality of care. However, widespread adoption of AI in healthcare has remained limited despite these advances, with one of the biggest challenges being trust in such AI systems. Thankfully, significant progress and advances have been made towards transparency- and trust-centric design, and in this seminar I will be discussing recent developments in quantitative explainable AI, trust quantification, improvements in data, and best practices and ethical considerations for trusted AI, as well as example use uses of how this enables the building of trusted AI solutions for healthcare.
This session is a part of the monthly seminar series organized by Spark: AI Health Initiative for Medical Imaging. The seminar will highlight outstanding work in medical imaging at Duke and beyond. The seminar recordings will be publicly available.
The Spark initiative focuses on development, validation, and clinical implementation of artificial intelligence algorithms for broadly understood medical imaging by bringing together the technical and clinical expertise across Duke campus. For more information please contact Dr. Maciej Mazurowski (firstname.lastname@example.org).