What Is the Role of Quality Management Systems for AI Devices?

Author: Rasit Dinc

Artificial intelligence (AI) is no longer a futuristic concept in healthcare; it is a present-day reality, with AI-powered medical devices transforming diagnostics, treatment planning, and patient monitoring. As these sophisticated technologies become more integrated into clinical practice, ensuring their safety, effectiveness, and reliability is paramount. This is where a robust Quality Management System (QMS) becomes indispensable. A QMS provides a structured framework for managing the entire lifecycle of AI medical devices, from initial design and development to post-market surveillance, ensuring they meet rigorous regulatory and quality standards.

The Foundational Role of a Quality Management System

A QMS serves as the backbone for the development and deployment of AI in healthcare. It offers a systematic and transparent approach to translate complex, research-based AI technologies into tangible clinical solutions that are safe and effective [1]. The primary goal of a QMS in this context is to bridge the "translation gap" between AI research and its practical application in patient care. By documenting processes, procedures, and responsibilities, a QMS ensures that AI devices are developed and maintained in a controlled and consistent manner, adhering to evolving regulatory requirements and promoting continuous improvement [1].

For healthcare organizations (HCOs), implementing an enterprise-wide QMS tailored to AI technologies is not just about compliance; it is about embedding a culture of quality and excellence. This proactive approach helps to minimize redundancies, align internal governance with a commitment to scientific rigor, and ultimately, accelerate the delivery of high-quality AI-driven healthcare solutions.

Key Components of a QMS for AI Devices

An effective QMS for AI devices is built upon three core pillars: People & Culture, Process & Data, and Validated Technology [1].

• People & Culture: A successful QMS implementation begins with fostering a proactive culture of quality. This involves ensuring that all stakeholders, from data scientists and engineers to clinicians and regulatory experts, are trained and aware of the principles of quality management and the specific requirements for AI devices. It requires a shift from siloed research initiatives to a collaborative, interdisciplinary approach to validation and oversight [1].

• Process & Data: The processes for developing, validating, and monitoring AI devices must be clearly defined and documented. This includes everything from data acquisition and annotation to model training, testing, and deployment. A risk-based approach is crucial, allowing organizations to focus their resources on areas of highest concern, such as data privacy, algorithmic bias, and patient safety [2].

• Validated Technology: The AI models and the software that contains them must undergo rigorous validation to ensure they perform as intended. This involves not only assessing the model's accuracy but also its robustness, interpretability, and generalizability. As AI technologies evolve, the QMS must also adapt to incorporate new validation methods and standards [3].

Impact on Existing Standards like ISO 13485

The integration of AI into medical devices has a significant impact on existing quality management standards, such as ISO 13485, which is the standard for medical device quality management systems. Organizations that develop AI-powered medical devices need to adapt their existing QMS to address the unique challenges posed by AI. This includes:

• New Processes and Documentation: AI introduces the need for new procedures related to data management, model training, and algorithm change control. These processes must be documented within the QMS.
• Enhanced Risk Management: The risk management process must be expanded to consider AI-specific risks, such as algorithmic bias, data poisoning, and the "black box" nature of some AI models.
• Post-Market Surveillance: The post-market surveillance plan must be updated to include monitoring the performance of the AI model in the real world and collecting data to support continuous improvement [3].

Shared Responsibility and the Path Forward

The responsibility for ensuring the quality of AI medical devices is shared between the vendors who develop the AI models and the healthcare organizations that use them. Vendors must provide detailed documentation about the model's performance, limitations, and intended use. Users, in turn, must ensure that the AI device is used appropriately and that its performance is monitored over time [2].

In conclusion, as AI continues to reshape the healthcare landscape, the role of Quality Management Systems will become even more critical. By providing a structured and systematic framework for managing the entire lifecycle of AI devices, a QMS is essential for ensuring that these transformative technologies are developed and deployed in a manner that is safe, effective, and worthy of the trust of both clinicians and patients.

References

[1] Overgaard, S.M., Graham, M.G., Brereton, T. et al. Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions. npj Digit. Med. 6, 218 (2023). https://doi.org/10.1038/s41746-023-00968-8

[2] Mercolli, L., Rominger, A., & Shi, K. (2024). Towards quality management of artificial intelligence systems for medical applications. Zeitschrift für Medizinische Physik, 34(2), 343–352. https://doi.org/10.1016/j.zemedi.2024.02.001

[3] 8foldgovernance. (2025, July 18). Expert Insights: How AI will impact your ISO 13485 Quality Management System. 8foldgovernance.com. https://8foldgovernance.com/expert-insights-how-ai-will-impact-your-iso-13485-quality-management-system/