Ethical Challenges from Medical Big Data and AI
FREE VIDEO LECTURE - Download 'Certificate of Attendance' after passing the QUIZ with 80% or higher.
Video Lecture Details
- Credits: 0.5
- Video Length: 35m, 38s
Method of Participation:
To receive a statement of credit, you must:
- Watch the video in its entirety
- Successfully complete the video post-test (80% or higher)
- Submit certificate completion information
- Receive the course evaluation
Big data and artificial intelligence are revolutionizing most areas of society, and are poised to make a similar impact on health care and medicine. Like all new powerful technologies, AI has risks as well as benefits. If not carefully managed, AI can contribute to serious harm to patients in areas including privacy and nosocomial injury. This presentation will describe the nature of these risks along with potential approaches to reduce harm.
Originally published on January 8, 2020
Brian R. Jackson, MD, MS
Medical Director, Business Development, IT and Support Services
Associate Professor of Clinical Pathology; Adjunct Associate Professor of Biomedical Informatics
University of Utah School of Medicine
After this presentation, participants will be able to:
- Identify key limitations of HIPAA in the big data/AI era
- Identify mechanisms of bias in black box algorithms
- Describe mechanisms for AI risk reduction through both public policy and academic research
University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories
NO FEE is associated with this continuing education activity. All CE credit granted on this website (arup.utah.edu) is provided as a service to medical laboratory professionals as part of the academic mission of the University of Utah’s Department of Pathology and ARUP Laboratories. After completing the post-test and submitting your information where prompted, a certificate will be generated and available for printing or saving.
Source: ARUP Lab
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