I. Glenn Cohen, "Big Data, AI, and Health Care, Legal and Ethical Issues"

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The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics

February 8, 2019, Black-Box Medicine: Legal and Ethical Issues: A Health Policy and Bioethics Consortium Black-box medicine—the use of opaque computational models to make care decisions—has the potential to shape health care by improving and aiding many medical tasks. For example, IBM Watson for Oncology is a machine-learning system that intends to help clinicians quickly identify essential information in patients’ medical records and explore treatment options for 13 cancers. However, it has only recently emerged that the recommendations Watson for Oncology gave for cancer treatments were “often incorrect” and that IBM kept this defect secret for over a year. What are the ethical and legal issues of black-box medicine? When do algorithms operate like a “black box“? How can we ensure that artificial intelligence technologies deliver what they promise?

Big Data, AI, and Health Care,
Legal and Ethical Issues
I. Glenn Cohen, JD
James A. Attwood and Leslie Williams
Professor, Harvard Law School
Director, Petrie-Flom Center for Health
Law Policy, Biotechnology and
Bioethics
igcohen@law.harvard.edu

w Will not be promoting
unlabeled/unapproved uses of
drugs, devices, products, protocols,
or therapeutic strategies.
w Serves as a bioethics consultant for
Otsuka Pharmaceuticals on digital
medicine portfolio.
Disclosure

Big Data, Predictive Analytics, Machine
Learning: Promise and Peril

Two Possible Ways of Dividing the World
Function Purpose
Imaging Democratizing Expertise
Prognostics Automating Drudgery
Diagnostics Optimizing Resources
Treatment Pushing Frontiers
Suggested by Nicholson Price, U Mich. Law School

Two Hypothetical Use Cases
• Choosing Cancer Therapeutics
• ICU Bed Allocation

Ethics of Building + Implementing Predictive
Analytics
Phase 1: Acquiring Data
• Consent
• Data Set Representativeness
• Governance
Phase 2: Building and Validating Model
• Auditing
• Transparency
• Trade Secrecy
Phase 3: Testing Model in Real World Settings
• Notice and Consent for Use on Patients?
• Liability
• Regulator Role
Phase 4: Broad Dissemination
• Equitable Access

Types of Health Data
I. Glenn Cohen & W.
Nicholson Price II
Privacy in the Age of
Medical Big Data, Nature
Medicine (2019)

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1. Big Data, AI, and Health Care, Legal and Ethical Issues I. Glenn Cohen, JD James A. Attwood and Leslie Williams Professor, Harvard Law School Director, Petrie-Flom Center for Health Law Policy, Biotechnology and Bioethics igcohen@law.harvard.edu

2. w Will not be promoting unlabeled/unapproved uses of drugs, devices, products, protocols, or therapeutic strategies. w Serves as a bioethics consultant for Otsuka Pharmaceuticals on digital medicine portfolio. Disclosure

3. Big Data, Predictive Analytics, Machine Learning: Promise and Peril

4. Current and Near Future Applications

5. Two Possible Ways of Dividing the World Function Purpose Imaging Democratizing Expertise Prognostics Automating Drudgery Diagnostics Optimizing Resources Treatment Pushing Frontiers Suggested by Nicholson Price, U Mich. Law School

6. Two Hypothetical Use Cases • Choosing Cancer Therapeutics • ICU Bed Allocation

7. Ethics of Building + Implementing Predictive Analytics Phase 1: Acquiring Data • Consent • Data Set Representativeness • Governance Phase 2: Building and Validating Model • Auditing • Transparency • Trade Secrecy Phase 3: Testing Model in Real World Settings • Notice and Consent for Use on Patients? • Liability • Regulator Role Phase 4: Broad Dissemination • Equitable Access

8. Safety and Effectiveness

9. Liability

10. Types of Health Data I. Glenn Cohen & W. Nicholson Price II Privacy in the Age of Medical Big Data, Nature Medicine (2019)

11. Thank you!

12. Digital Medicine: FDA approval of First Pill with Sensor

13. Digital Medicine: FDA approval of First Pill with Sensor

I. Glenn Cohen, "Big Data, AI, and Health Care, Legal and Ethical Issues"

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