1. Regulation and Legislation of AI/ML
Nicholson Price, Jon Burch, & Doug McNair

2. Scope Clinical AI Regulation, legislation (highlights)
Inform/make decisions about individuals
Diagnosis
Treatment recommendations
Regulation, legislation (highlights)
Regulation
Privacy
Data
[Not really reimbursement]

3. Key issues: Explainability
To whom?
Regulators
Clinicians
Patients
Performance/explainability tradeoff
Validation without explanation
Opacity & hidden bias

4. Key issues: Regulation
Static/locked v dynamic/continuously learning
RWD/RWE v Clinical trials
Hazard analysis, risk-based
Many existing tools
Post-market surveillance

5. Key issues: Privacy Loci of privacy risks
Initial development: data
Validation/sharing
Inferences
De-identification (unhelpful)
Consent (challenging)

6. Key issues: Data Paths Large entities Collaborations Absence
Government/infrastructure (PMI/AllofUs)

7. Takehomes Some things aren’t that different Some are Balances
Validation (validity, utility)
Risk measures
Some are
Opacity (~); hidden bias
Continuous learning/dataset shift
Balances
Privacy/data infrastructure
Risk/status quo

8. Key Recommendations Black boxes are OK; validate Validation:
Independent data
Postmarket surveillance
Collaborative governance
Combat bias at regulatory level
Data infrastructure