1 / 8

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].

musser
Télécharger la présentation

Regulation and Legislation of AI/ML

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related