Algorithms as medical devices

MSR-TR-2019-48 |

Published by PHG Foundation

From wearables for health monitoring and self-care apps, to machine learning analysis of medical images, the potential of digital technologies to revolutionise healthcare has commanded many headlines. Realising the medical benefits of such technologies needs appropriate regulation. In reality, identifying where a device fits within the complex and evolving regulatory environment is far from simple.

The rapid growth of digital devices, software and technologies means that the medical device sector is changing. Many small and independent manufacturers are encountering medical device regulation for the first time. At the same time, responsive and effective regulation of digital devices requires sound understanding of the underlying new technologies and concepts.

Algorithms as medical devices describes how digital health is covered by existing medical device regulation and outlines three critical areas:

The challenges that the digital health sector may pose for regulators and developers
How digital devices can be regulated as medical devices under UK/EU and US law
The specific problems that machine learning could pose to medical device regulation
A resource for regulators and policy makers, this report makes recommendations for improving the regulation of digital medical devices.

Responsible, Equitable, and Ethical AI panel discussion

This timely panel discussion did not air during Microsoft Research Summit 2022 but was released during the event as part of the Microsoft Health Futures Forum. AI and ML technologies have the potential to truly transform health and life sciences research. However, making sure that AI systems are developed and deployed responsibly, equitably, and ethically, is one of the toughest challenges facing AI practitioners. As we’ve seen in countless media articles, AI systems have the potential to behave unfairly or unreliably. They can generate potentially harmful predictions, and they can reproduce or exacerbate existing social inequities. And, they can be very difficult to understand. Many questions arise when we consider the deployment of AI into clinical settings. For example, what guiding principles are needed to…