Summary
Recommends risk-based oversight for AI that relies on laboratory data, modernization of laboratory regulation, harmonized data and reporting, diverse validation data, clear stakeholder responsibilities, independent verification, and continuous monitoring for accuracy, drift, safety, and bias.
Healthcare Implications
Clinical laboratories should validate and continuously monitor AI performance using quality-management practices analogous to laboratory quality control. Developers should provide the data and technical information needed for independent verification, while regulators should clarify oversight and validation expectations.