How MedTech manufacturers can move cybersecurity upstream, strengthen traceability, and prepare for faster vulnerability response.
Complaint volumes have outgrown manual triage, and medical device companies are turning to AI to keep up. The speed gains are real, but used carelessly the same tools can bury a reportable event at the bottom of a queue while the MDR clock runs. This piece maps where AI genuinely helps in post-market surveillance, where human judgment has to stay, and what validation, monitoring, and audit trails a defensible program needs. It is grounded in the current regulatory frame, including the QMSR and FDA’s new inspection program (CP 7382.850) that took effect February 2, 2026, 21 CFR Part 803 reporting timelines, and ISO 13485 software validation expectations. It includes a practical table drawing the line between tasks that are safe to automate and decisions that must stay human-owned. The intended audience is quality, regulatory, and post-market surveillance professionals in the medical device industry.
As healthcare shifts towards remote monitoring, connected devices and decentralized care, sensor technologies are becoming increasingly foundational to how medical devices collect, validate and transmit critical information. From remote patient monitoring and at-home care to connected medical devices and robotic-assisted surgery, sensing technologies can help enable earlier detection, more reliable clinical data and better-informed decision making.
GenAI-enabled medical devices hold transformative promise for patient care and the broader health ecosystem. At the same time, these devices may introduce unique risks when compared to traditional software and AI-enabled medical devices.
Catastrophic failures rarely stem from isolated hardware bugs. They breed where hardware, firmware, wireless telemetry, and clinical workflows intersect. These silent data integrity failures can invalidate entire research efforts without real-time detection, squeezing V&V Engineers between exploding complexity and compressed timelines.
Medical device software development teams have always struggled with a deceptively simple question: How much of the system is actually complete?
Zimmer Biomet will hire 500 professionals over the next three years for its new Bengaluru technology centre, expanding its AI, software engineering, and R&D capabilities as India strengthens its position as a global healthcare innovation hub.
AdaptHealth is investigating a material cyberattack after attackers used a social engineering scheme to compromise a third-party contractor’s account, stealing patient data from its cloud-based systems as reports link the breach to the ShinyHunters extortion group, though the attribution remains unconfirmed.
MTI Viewpoints Insights shared by industry relative to healthcare and the advancement of medical technology. Jaimon Jose, senior director of engineering and compliance at CharmHealth, is a software architect and technical leader with more than 18 years of product development experience in the areas of identity and security technologies, distributed systems, cloud security, and data…
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.