J. David Giese

This article provides an in-depth exploration of medical device cybersecurity requirements, including best practices and FAQs. It also includes examples and resources for those looking to implement or improve their own threat modeling processes.

A concise guide to the FDA’s De Novo pathway for novel, moderate-risk medical devices that lack a suitable predicate. It includes real-world examples and offers insider tips on managing higher costs, extended timelines, and proactive FDA engagement to successfully navigate this classification route.

Practical suggestions and tips for authoring SBOMs for medical devices and for using them to monitor for cybersecurity vulnerabilities.

A Pre-Sub is a mechanism for requesting formal written feedback from the FDA, and (optionally) a one-hour meeting. This article provides best-practices and FAQs about pre-subs.

A comprehensive FAQ for people who are interested in creating a new medical device software to learn the basics about the US regulations.

Medical device design begins with design inputs. The FDA says developing your design inputs is “the single most important design control activity,” yet writing good design inputs is difficult. This article presents Innolitics’ answers questions our clients frequently ask us about design inputs and analyzes a number of poorly written example requirements.

Wondering whether FDA’s Breakthrough Device Designation or STeP program is worth the effort? This pocket guide unpacks the program in plain English—what it is, who qualifies, and exactly how it can slash review delays, unlock CMS reimbursement shortcuts, and impress investors.

To provide quality service to our clients, we need to do more than produce quality work.

In this article, we share a nuanced approach to avoiding sharing sensitive information in an SBOM.

The discussion explores generative AI (GenAI) in medical devices, focusing on FDA's considerations for regulation, risks, and the lifecycle management of such technologies. Key points include the complexity of training data in foundation models, the risks of hallucinations in outputs, and the evolving role of post-market surveillance. Insights highlight challenges in balancing innovation with regulatory oversight and safety.