Ensuring that proven therapies can be delivered safely and accessibly to the patients who need them provides opportunity in what we innovate and in how broadly those innovations can reach.
Without formal policies and governance, MedTech organizations face risks to intellectual property, product quality, and ultimately patient safety. Thoughtful AI governance enables development teams to capture efficiency gains while maintaining the rigor that the industry demands.
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.
Next generation mitral valve, designed to facilitate patient lifetime management reports first uses in sternotomy, Minimally Invasive Cardiac Surgery (MICS) and robotic cases.
SUNRISE-II feasibility trial marks the company’s entry into U.S. clinical development, advancing minimally invasive, anesthesia-free treatment approach
Edwards announced 10-year results from the COMMENCE aortic trial, reinforcing the long-term durability and sustained performance of its proprietary RESILIA tissue.
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?
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.
No longer a horizon technology, Artificial Intelligence in healthcare has reached a level of real-world performance that makes clinical value demonstrable at a critical time in healthcare – a time of clinician shortages, backlogs, and rising costs that have made access to treatment a challenge.
MedTech leaders must find a balance between the slow, careful world of medicine and the fast-paced expectations of investors. Real success comes from choosing high-quality science over quick shortcuts, as being thorough is the only way to build lasting trust and reach the market.
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.
A long-overdue push to reduce administrative friction, improve access to patient data, and move the system away from workflows that continue to waste time for both patients and providers.
Having already set expectations around the structured capture of medical device safety data, regulators are now ready to analyze related insights, and they expect device manufacturers to match this capability.