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AI in Health Care: Critical Data Regulations
Iliana Peters, Liz Harding and Lindsay Dailey, Speakers
William A. Tanenbaum, Moderator
Agenda
Introductions
What is Artificial Intelligence (AI)?
How AI is Used in Health Care (HC)?
Data Regulations & Privacy Trends
Best Practices & Key Takeaways
What is AI?
Self-Driving Cars
Food Delivery Droids
Pharmacy Droids
AI is More Than Just Robots
Machine Learning
Big Data Analysis
Behavioral Analytics
Augmented Intelligence
How is AI Used in Health Care?
Current Applications in Health Care
Natural-Language Processing for Research and Clinical Decision Support
Genomic Data and Research
EHR Data and Research
Voice-to-Text Transcription
Medical Imaging
Robot-Assisted Surgery
Fraud Detection
Cybersecurity
Natural-Language Processing for Research and CDS
https://www.youtube.com/watch?v=jA87BjmibeI
Genomic Data and Research
www.cancer.gov
EHR Data and Research
http://svn.bmj.com/content/early/2017/07/29/svn-2017-000101
Voice-to-Text Transcription
https://www.youtube.com/watch?v=GYiU3hyMMxs
Medical Imaging
https://aws.amazon.com/blogs/startups/adapting-deep-learning-to-medicine-with-behold-ai/
Robot-Assisted Surgery
https://www.kansanmedtrip.com/ROBOTIC-HEART-SURGERY.php
Fraud Detection
https://azati.com/fighting-insurance-fraud-with-ai-based-solutions/
Cybersecurity
https://www.techrepublic.com/article/mit-shows-how-ai-cybersecurity-excels-by-keeping-humans-in-the-loop/
Data Regulations & Privacy Trends
Relevant Laws
GDPR
HIPAA
CCPA
Similar state laws
Recent Guidance
AMA: Augmented intelligence in health care https://www.ama-
assn.org/system/files/2019-01/augmented-intelligence-policy-report.pdf
NIST: An Application of Combinatorial Methods for Explainability in Artificial Intelligence and Machine Learning https://csrc.nist.gov/CSRC/
media/Publications/white-paper/2019/05/22/combinatorial-methods-for-explainability-in-ai-and-ml/draft/documents/combinatorial-methods-explainability-ai-ml-draft.pdf
Data Privacy Issues
Data Aggregation: Is any information really de-identified?
Machine Learning: How far should machine learning be allowed to go?
Behavioral Analysis: AI predictive capabilities have privacy implications.
Other Potential Data Disclosures: To whom is your robot giving your data?
Data Security Issues
Networked Devices: IoT devices are generally vulnerable.
Risk Analysis and Risk Management
Access Controls
Data Repositories: Large amounts of data mean large amounts of risk.
Back Doors: Who built your robot?
Machine Learning: The dark side can use it too!
Best Practices & Key Takeaways
Common Concerns
Machines Only Do What you Tell Them To Do!
Who’s your engineer?
Societal Implications: “Diminished Resilience”
Best Practices and Check List
Understand your technology/tool
Build in Privacy by Design and DPIAs
Prepare FAQs for internal and external use
Develop a process for data subject requests
Questions?
Iliana L. Peters, J.D., LL.M., CISSP, Washington, D.C. Office Health Care Privacy [email protected]
Liz Harding, Denver Office GDPR, Privacy and Technology Licensing [email protected] Lindsay Dailey, Chicago Office Health Care Services Group [email protected]
William Tanenbaum, New York Office Practice Co-Chair, Health Care Technology & Innovation [email protected]
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