post market surveillance
post market surveillance

Why Organizations Miss Emerging Product Risk: Understanding the engineering mechanisms that remain hidden long before complaint trends become visible

By Aalap Patel, MS, CQE, CSSBB

Emerging medical-device risks are often missed because early warning signs are fragmented across complaints, manufacturing, suppliers, returned products, clinical observations, and other data sources; not immediately as statistically significant trends. An engineering-centered, cross-functional approach connecting these weak signals to underlying mechanisms sooner, can improve designs, verification, manufacturing controls, and risk management before isolated issues become widespread quality problems.

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AI Compliance in Medical Devices
AI Compliance in Medical Devices

Is Your Organization Ready to Govern AI in Regulatory Affairs?

By Anthony Watson, Geethapriya (Priya) Setty, Jay Vaishnav, PhD

By the time most medtech organizations noticed their teams were using AI in regulatory work, the governance conversation was already running behind.. How can your organization move forward? An Operational AI Capability Maturity Model — a five-domain framework built specifically for regulatory affairs and offers a direct way to assess where your organization actually stands. If your team cannot consistently answer who approved the tool, how the output was verified, and what was retained, this framework is a practical place to start.

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QMS Interoperability
QMS Interoperability

Chicken or the Egg: Should Device Interoperability or QMS Interoperability Come First?

By Carl Washburn

To achieve medical Device Interoperability, system boundaries need to be defined, system architecture needs to be aligned, and interfaces and communication protocols need to be established across individual components of the medical device. In some cases, it is as important to design and implement QMS Interoperability as it is to design Device Interoperability.

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Safety of AI in Medical Devices
Safety of AI in Medical Devices

AI in Medical Devices: Safety Questions the Industry Can’t Afford to Ignore

By Pujitha Gourabathini

Artificial intelligence is moving quickly into mainstream medical devices, and the industry has become fluent in a familiar set of concerns: bias, transparency, and cybersecurity. These topics matter, but they don’t capture the risks most likely to shape patient safety in the coming decade. The deeper challenges lie in the interactions between algorithms, clinical workflows, data pipelines, and human decision making. Those interactions are where safety is won or lost, and they remain the least examined part of AI adoption.

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