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.
Highlights
Artificial intelligence (AI) is a broad discipline that aims to understand and design systems that display properties of intelligence [1]
To optimize on the benefits associated with software as a medical device (SaMD), patient safety and effectiveness need to be aptly assessed for which 2 key steps are necessary
International standards and guiding principles addressing the uniqueness of SaMD with a continuous learning algorithm are required [14], outlining best practice oversight and reporting requirements
Summary
Artificial intelligence (AI) is a broad discipline that aims to understand and design systems that display properties of intelligence [1]. To verify claims of safety and effectiveness in the form of submitted evidence, regulators must keep pace with the complexity of algorithm models, including validation and testing stages, selected use of software of unknown pedigree, and real-world performance [7]. The “patient-centered” approach referred to by the FDA addresses usability, equity, trust, and accountability Engagement with both developers and end users occurred at a February 2020 Public Workshop on the Evolving Role of Artificial Intelligence in Radiological Imaging. At the latter event, The American College of Radiology (ACR) and Radiological Society of North America (RSNA) questioned [13] the ability of the FDA to ensure safety and effectiveness of continuous learning algorithms, without direct physician or expert oversight during each use. Evaluation of real-world algorithm performance will reassure patients and health professionals of readiness for clinical use
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