Devices are Getting Smarter, Making Materiovigilance Evolve Faster

By Dr. Padmalakshmi Jnaneshwar

According to Deloitte, only 22% of life sciences leaders say they have successfully scaled AI, and just 9% report achieving significant returns. For materiovigilance, this highlights the growing gap between execution and team expectations. Within this gap, fragmented data and manual processes continue to slow response times and increase compliance risk, creating a disconnect between the promise of real-time safety monitoring and operational reality. How can organizations close this execution gap and evolve materiovigilance from a reactive compliance function into a proactive, data-driven discipline?

Continue reading →

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.

Continue reading →

Managing AI in Medical Technology: From Innovation to Compliance

By Ali Behbahani, Dr. Ibrahim Halfaoui

Companies developing or deploying AI systems now face increasing scrutiny around risk classification, transparency and lifecycle governance. An AI management system (AIMS) aligned with ISO/IEC 42001 provides a structured way to address these challenges while significantly improving efficiency, quality, and innovation outcomes — and ensuring AI system are  effective, safe, compliant, and trustworthy.

Continue reading →