Critical Challenges in ML-Based SaMDs prompting FDA Action Plan

Max Kelsen
4 min readJan 27, 2021

Author: Navid Toosi Saidy, Quality and Technology Translation Lead at Max Kelsen

At Max Kelsen, we’re developing a range of advanced machine learning-based Digital Health solutions for a range of pressing issues across the healthcare and life sciences industry. We envision this next generation of software tools will be vital to improving healthcare services, diagnostics and disease or injury management and assist healthcare workers to provide the best quality of care to patients.

Software used for diagnostics, prevention, treatment or monitoring of a disease fall into a category called ‘software as a medical device’ (SaMD) and are closely monitored and regulated by authorities such as the Food and Drug Administration (FDA) in the USA, as well as Australia’s Therapeutic Goods Administration (TGA).

While recent endeavours by the International Medical Device Regulators Forum (IMDRF) have provided better clarity around quality management and translation of SaMDs, regulation authorities are under mounting pressure to consider the implications of the fast-paced development of artificial intelligence (AI) and machine learning (ML)-based SaMDs.

Just a couple of weeks ago, the US FDA published an action plan for safe and patient-centric regulation of AI and ML-based SaMD. This action plan has been developed with significant engagement from medical device stakeholders on FDA’s discussion paper released in early 2019 called “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based SaMD”. Overall, the FDA has proposed a “multi-pronged approach” which will be dedicated to providing better oversight of AI/ML-based SaMDs through a five-part initiative that aims to ensure patient safety and algorithm transparency.

Here, we briefly summarise some of the critical challenges in ML-based SaMDs, which have prompted each initiative within the action plan, and discuss the next steps in regulating these exciting new software tools.

1. Tailoring Regulatory Framework for AI/ML-based SaMD

Traditionally, and in the current regulatory environment, modifications to regulatory approved and cleared SaMDs may…



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