Dr.ssa Paola Antonini, Meditrial scientific director
The International medical device regulators forum (Imdrf), describes Software as a Medical Device (SaMD) as software that can run on generic (non-medical) computing platforms. They can be used in conjunction with other medical devices or other hardware/software. SaMD, which itself is a medical device, is one of three types of software related to medical devices. The other two types include software that is an integral part of a medical device and software used in the manufacture or maintenance of a medical device. SaMDs represent a revolution in digital health technology that can perform complex medical functions, diagnose conditions, indicate treatments and support clinical management. The use of software as a medical device continues to grow. SaMDs can be used for many purposes, including imaging and monitoring specific medical functions or parameters, aiding the health care provider in the diagnostic-therapeutic pathway.
SaMD: REGULATORY PANORAMA
Given the unique characteristics of software as a medical device, which go beyond those of a traditional medical device or hardware, regulatory authorities have recognized the need to converge on a framework of common principles, enabling all stakeholders to promote innovation and protect patient safety. In 2013, the IMDRF established the Software as a Medical Device Working Group (WG) to develop guidelines to support innovation and timely global access to safe and effective SaMDs. Chaired by the FDA, the SaMD-related WG agreed:
- key definitions for SaMDs;
- The quality management system to be applied to SaMDs;
- The clinical evaluation of SaMDs;
The Imdrf Software as a medical device working group (WG) has therefore published a framework on the risk classification of SaMDs (SaMD: Possible framework for risk categorization and corresponding considerations). The recommendations provided in this document enable manufacturers and regulators to more clearly identify SaMD risk categories. These categories are based on the potential levels of impact the device output may have on the patient or public health. The document published by IMDRF classifies SaMDs into four different risk categories.
Device classification is, therefore, closely related to the criticality of the information provided by the SaMD to treat, diagnose, guide, or inform the clinical procedure. The accuracy of such data can be critical to avoid harmful health effects. The Level IV category is the SaMD with the greatest impact on the patient or public health and Level I is for the least impact.
A second document published by Imdrf is on the quality management of SaMDs (SaMD: Application of Quality management system). This document helps manufacturers and international regulatory authorities achieve greater understanding and a common language for the application of quality management system requirements to be applied to SaMDs throughout their life cycle. This document provides harmonized quality management principles that the Fda, along with other regulatory bodies, can adopt within their own regulatory frameworks.
Finally, it is worth noting that health care decisions increasingly use on the information provided by the SaMD. For these reasons, The Fda published the guideline: SaMD: Clinical evaluation – Guidance for Industry and Food and drug administration staff to describe the principles and approaches to be taken in the clinical investigation to demonstrate the safety, efficacy, and performance of software as a medical device. The guidance focuses on the activities to be undertaken by SaMD manufacturers for appropriate clinical evaluation of their software as a medical device.
Artificial intelligence (Artificial Intelligence-AI) has been defined as the science of making intelligent machines. Artificial intelligence can use a variety of techniques, such as models based on statistical data analysis, evolved systems that rely primarily on “if-then” relationships, and machine learning (Machine learning-ML).
Machine learning is an artificial intelligence technique that is used to design algorithms intended to learn and act on received data. Software developers can use machine learning to create algorithms that are “Locked,” whose function does not change over time, or “Adaptive” so that their behavior changes based on new data.
Some examples of artificial intelligence and machine learning technologies include, for example, an imaging system that uses algorithms to provide diagnostic information.
As a result, technology has rightfully entered every aspect of the health care system, enabling its rapid development. Software incorporating artificial intelligence, and in particular the subset of AI known as machine learning, has become a relevant part of increasing numbers of medical devices.
One of the major advantages of using AI/ML in software lies in its ability to learn from real-world use and experience and progressively improve performance. The new information obtained from SaMDs using AI/ML, therefore, can be a fundamental therapeutic diagnostic support.
Over the past decade, the Fda has reviewed and authorized an increasing number of devices incorporating AI/ML, with applications in various fields of medicine. Interest in medical devices incorporating ML functionality has thus increased, and this trend is expected to continue.
FDA GUIDING PRINCIPLES ON SaMDs
The use of AI and ML brings with it certain issues, such as the need to continually monitor, identify and manage the risks associated with these technologies, as well as implications pertaining to security, privacy and ethics.
Aiming to address these issues, in 2021, the Fda together with Health Canada and the U.K. Regulatory Agency (Mhra) jointly identified 10 guiding principles (Good machine learning practice for medical device development) to encourage and promote the development of safe, effective, and high-quality medical devices using AI and ML.
The Fda’s 10 guidelines cover a variety of issues, but many of them revolve around the technology development model, cybersecurity (cybersecurity) and risk mitigation requirements, as well as the need to involve as many experts as possible in the development and maintenance of SaMDs that rely on AI and ML.
These principles can help health systems that now rely on AI/ML medical devices to better understand and manage areas of criticality, as well as digital health startups and biotechnology companies to develop safe, effective, and high-quality AI/ML medical devices.
The guiding principles have as their primary goal the development of best practices for AI/ML technology development. However, The most challenging task for regulators and industry will be the development of targeted policies and procedures that fully integrate with existing quality regulations.