The most recent AI thought leadership published on MedTechIntelligence.com.
Why is algorithmic transparency becoming an increasingly important consideration in FDA’s review of AI/ML medical devices?
Many challenges of designing and validating pediatric digital health devices are over-looked across developmental stages. Regulatory strategy, human factors, software architecture, and algorithm performance are critical consideration in dynamic patient populations.
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.
Rene Zoelfl, Global Industry Advisor for PTC’s MedTech practice, shares how intelligent product lifecycle at Fresenius Medical Care connects cross-discipline teams through a digital fabric built on a shared data foundation.
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.
While off-label use may be permissible, contraindicated use still has a strong regulatory boundary as reinforced by FDA guidance finalized in 2025.
Global look at regulatory compliance, guidance, trends and deadlines.
The medical device industry faces growing pressure to align U.S. and EU regulations, with the EU’s Medical Device Regulation (MDR) and the FDA’s Quality Management System Regulation (QMSR) setting new benchmarks.
The Service+Tech model embeds AI into the workflow of regulatory experts rather than treating technology as a standalone tool; an approach allowing organizations to adopt AI immediately, with no risk and no R&D investment, while keeping full confidence in the accuracy and regulatory readiness of submissions.