JMIR AI· 2025Q1
Predetermined Change Control Plans: Guiding Principles for Advancing Safe, Effective, and High-Quality AI-ML Technologies
- 44citations
- Q1SCImago
- 2025year
Short summary
Predetermined change control plans (PCCPs) offer a regulatory pathway to streamline approval for adaptive AI-ML technologies, reducing lengthy waits and repeated submissions for software updates.
AI-generated from the title and abstract; the full text is not read.
Key points
- AI-ML technologies' adaptive nature challenges existing regulatory frameworks.
- Current approval timelines for AI-enabled devices can exceed one year.
- Predetermined change control plans (PCCPs) are a proposed FDA solution.
- PCCPs aim to reduce repeated approval requests for software updates.
- This approach seeks to balance innovation with safety and compliance.
AI-generated from the title and abstract; the full text is not read.
Abstract
Unlabelled: The adaptive nature of artificial intelligence (AI), with its ability to improve performance through continuous learning, offers substantial benefits across various sectors. However, current regulatory frameworks are not intended to accommodate this adaptive nature, and they have prolonged approval timelines, sometimes exceeding one year for some AI-enabled devices. This creates significant challenges for manufacturers who must deal with lengthy waits and submit multiple approval requests for AI-enabled device software functions as they are updated. In response, regulatory agencies like the US Food and Drug Administration (FDA) have introduced guidelines to better support the approval process for continuously evolving AI technologies. This article explores the FDA's concept of predetermined change control plans and how they can streamline regulatory oversight by reducing the need for repeated approvals, while ensuring safety and compliance. This can help reduce the burden for regulatory bodies and decrease waiting times for approval decisions, therefore fostering innovation, increasing market uptake, and exploiting the benefits of artificial intelligence and machine learning technologies.
The authors' abstract, as published at the source. JMIR AI, 2025 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Health Information Management
Health Information ManagementHealth Professions