Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device

Jane Elizabeth Carolan; John McGonigle; Andrea Dennis; Paula Lorgelly; Amitava Banerjee

doi:10.2196/34038

Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device

JMIR Med Inform. 2022 Jan 27;10(1):e34038. doi: 10.2196/34038.

Authors

Jane Elizabeth Carolan^{1

2

3}, John McGonigle³, Andrea Dennis³, Paula Lorgelly², Amitava Banerjee^{1

4

5}

Affiliations

¹ Institute of Health Informatics, University College London, London, United Kingdom.
² Institute of Epidemiology and Health Care, University College London, London, United Kingdom.
³ Perspectum Ltd, Oxford, United Kingdom.
⁴ University College London Hospitals NHS Foundation Trust, London, United Kingdom.
⁵ Barts Health NHS Trust, London, United Kingdom.

PMID: 35084352
PMCID: PMC8832257
DOI: 10.2196/34038

Abstract

Artificial intelligence (AI) is a broad discipline that aims to understand and design systems that display properties of intelligence. Machine learning (ML) is a subset of AI that describes how algorithms and models can assist computer systems in progressively improving their performance. In health care, an increasingly common application of AI/ML is software as a medical device (SaMD), which has the intention to diagnose, treat, cure, mitigate, or prevent disease. AI/ML includes either "locked" or "continuous learning" algorithms. Locked algorithms consistently provide the same output for a particular input. Conversely, continuous learning algorithms, in their infancy in terms of SaMD, modify in real-time based on incoming real-world data, without controlled software version releases. This continuous learning has the potential to better handle local population characteristics, but with the risk of reinforcing existing structural biases. Continuous learning algorithms pose the greatest regulatory complexity, requiring seemingly continuous oversight in the form of special controls to ensure ongoing safety and effectiveness. We describe the challenges of continuous learning algorithms, then highlight the new evidence standards and frameworks under development, and discuss the need for stakeholder engagement. The paper concludes with 2 key steps that regulators need to address in order to optimize and realize the benefits of SaMD: first, international standards and guiding principles addressing the uniqueness of SaMD with a continuous learning algorithm are required and second, throughout the product life cycle and appropriate to the SaMD risk classification, there needs to be continuous communication between regulators, developers, and SaMD end users to ensure vigilance and an accurate understanding of the technology.

Keywords: Artificial intelligence; algorithm; informatics; machine learning; risk assessment; software.

©Jane Elizabeth Carolan, John McGonigle, Andrea Dennis, Paula Lorgelly, Amitava Banerjee. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.01.2022.