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# QA Practice for AI Devices is Priority: FDA
- URL: https://www.fdaweb.com/qa-practice-for-ai-devices-is-priority-fda/
- Published: 2024-06-17T12:00:00.000Z
- Updated: 2026-09-14T14:33:29.000Z
- Author: David McFarland
- Tags: Devices, #legacy-id-D5157191

Developing a medical device “quality assurance practice” for artificial intelligence (AI) models should be a priority to ensure clinical outcomes are optimal with the use of AI models that are “accurate, reliable, ethical, and equitable,” a new [online agency post](https://www.fda.gov/medical-devices/digital-health-center-excellence/blog-promise-artificial-intelligence-holds-improving-health-care?utm%5Fmedium=email&utm%5Fsource=govdelivery) says.

“Top of mind for device safety is quality assurance applied across the lifecycle of a model’s development and use in health care,” CDRH Digital Health Center of Excellence director **Troy Tazbaz** says in the post. *“*Continuous performance monitoring before, during, and after deployment is one way to accomplish this, as well as by identifying data quality and performance issues before the model’s performance becomes unsatisfactory.”

In the coming weeks, Tazbaz says the agency will elaborate on the following AI-related topics to help promote the use of AI in medical devices:

- Standards, best practices, and operational tools
- Quality assurance laboratories
- Transparency and accountability
- Risk management for AI models in health care

Tazbaz says that standards, best practices, and operational tools can help reinforce responsible AI development, adding that “principles such as transparency and accountability can help stakeholders feel comfortable with AI technologies. Quality assurance and risk management, right-sized for health care institutions of all sizes, can help provide confidence that AI models are developed, tested, and evaluated on data that is representative of the population for which they are intended.”

Across the U.S., solution developers, healthcare groups and the federal government are exploring and developing best practices for AI quality assurance in healthcare settings. “These efforts, combined with FDA activities relating to AI-enabled devices, may lead to a world in which AI in health care settings is safe, clinically useful, and aligned with patient safety and improvement in clinical outcomes,” Tazbaz says.