The MedTech Intelligence Industry Survey is a primary research initiative designed to capture the perspectives of professionals working across the medical technology sector. The study is intended to examine the pressures, priorities, technology shifts, operational challenges, and strategic changes influencing MedTech organizations in 2026.
Reverse engineering carries a reputation problem in medical device circles; it sounds like copying. In practice, it’s a legitimate engineering discipline with specific, defensible use cases: replacing obsolete components, improving an existing design’s manufacturability, and indigenizing imported parts. This article looks at when reverse engineering genuinely makes sense in medical device development, the technical process involved, and the intellectual property considerations teams need to navigate before using it.
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.
Medical device software development teams have always struggled with a deceptively simple question: How much of the system is actually complete?
The most recent AI thought leadership published on MedTechIntelligence.com.
Healthcare environments are dynamic with patient populations, clinical practices and data collection methods continuously evolving. Similarly, effective AI systems depend on more than performance at initial deployment. They must be monitored and managed throughout their lifecycle to remain reliable, clinically relevant, and safe.
Validation documentation should define process parameters, monitoring strategies, and operating ranges that can support future production increases. How does a practical framework for validation, revalidation, and process control help during medical device scale-up?
Human factors engineering plays a critical role in the design of AI-enabled medical devices and whether they might improve care or introduce new risks.
New implementation community builds on global collaboration to improve
real-time device data exchange for AI-enabled care.
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.
