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Regulation and Legislation of AI/ML

Regulation and Legislation of AI/ML. Nicholson Price, Jon Burch, & Doug McNair ( wnp@umich.edu ). Scope. Clinical AI Inform/make decisions about individuals Diagnosis Treatment recommendations Regulation, legislation (highlights) Regulation Privacy Data [Not really reimbursement].

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Regulation and Legislation of AI/ML

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  1. Regulation and Legislation of AI/ML Nicholson Price, Jon Burch, & Doug McNair (wnp@umich.edu)

  2. Scope • Clinical AI • 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 • 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

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