Predetermined Change Control Plans: Guiding Principles for Advancing Safe, Effective, and High-Quality AI-ML Technologies
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.
Authors
- Guilherme Prado Barbosa (ORCID: https://orcid.org/0009-0005-5001-5539)
- Dulce A. Oliveira (ORCID: https://orcid.org/0000-0002-8758-471X)
- Eduardo Carvalho (ORCID: https://orcid.org/0009-0005-0729-7008)
- Sandra Balseiro (ORCID: https://orcid.org/0000-0003-3721-8233)
- Rita Moura (ORCID: https://orcid.org/0000-0003-3042-9257)
- Ana Guerra (ORCID: https://orcid.org/0000-0003-3979-0874)
- Miguel Mascarenhas (ORCID: https://orcid.org/0000-0002-0340-0830)
- Ricardo Correia (ORCID: https://orcid.org/0009-0005-1237-6632)
- Francisca Pinheiro (ORCID: https://orcid.org/0009-0002-3549-5907)
- Nilza Ramião (ORCID: https://orcid.org/0000-0002-4633-8600)
- André Santos (ORCID: https://orcid.org/0000-0002-9676-6128)
Institutions
- University of Lisbon (PT)
- Universidade do Porto (PT)
- Hospital de São João (PT)
- Institute of Mechanical Engineering and Industrial Mangement (PT)
- University of Coimbra (PT)
- University of South Alabama (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-10-31
- DOI
- https://doi.org/10.2196/76854
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.27