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AI models in healthcare must not only be accurate but also explainable. Regulators like the FDA, EMA, and NMPA require Explainable AI (XAI) to ensure that AI-driven decisions are understandable, interpretable, and justifiable—especially for high-risk applications like diagnostics.

ISO 13485 is the international standard for quality management systems (QMS) in medical devices. It outlines the requirements for designing, developing, manufacturing, and distributing medical devices that consistently meet regulatory and customer requirements. Compliance with ISO 13485 is essential for market access, regulatory approval, and ensuring product safety and effectiveness.

ISO 14971 is the international standard for risk management in medical devices, providing a structured approach to identifying, evaluating, and controlling risks throughout the product lifecycle. Compliance with ISO 14971 is essential for meeting regulatory requirements such as the FDA’s Quality System Regulation (QSR), the EU Medical Device Regulation (MDR), and ISO 13485. Implementing a strong risk management system helps companies reduce product failures, enhance patient safety, and ensure regulatory approval.

Quantitative risk assessment (QRA) models are essential tools for evaluating and managing risks in medical device development. Unlike qualitative methods, which rely on subjective judgment (e.g., low, medium, high risk), QRA assigns numerical values to risk factors, allowing for data-driven decision-making. This approach is crucial for meeting regulatory standards like ISO 14971, FDA QSR, and EU MDR, ensuring objective risk evaluation, better risk control, and compliance

Failure Modes and Effects Analysis (FMEA) is a structured risk management method used to identify and prevent potential failures in medical devices. It is widely used to comply with ISO 14971, FDA QSR, and EU MDR by systematically analyzing failure risks, prioritizing them, and implementing controls to enhance patient safety and device reliability.

In the fast-evolving medtech landscape, Electronic Data Capture (EDC) platforms are essential for clinical trials, post-market surveillance, and regulatory compliance. But building one is just the beginning. Turning an EDC platform into a viable commercial product is a different challenge altogether. At ITR VN, we’ve partnered with multiple European and North American companies to help develop and scale their EDC platforms. We’ve seen first-hand what works—and what doesn’t. Here’s a look at the real-world journey from prototype to product.

As medical technology advances, miniaturized PCBs with ultra-low-power consumption, wireless charging, and extreme biocompatibility are becoming critical for next-generation wearable and implantable devices. These technologies enable continuous health monitoring, real-time data transmission, and long-term patient comfort. However, achieving these features presents significant design and engineering challenges.

As artificial intelligence (AI) becomes more integrated into critical sectors like healthcare, finance, and manufacturing, organizations face increasing regulatory and ethical scrutiny. ISO/IEC 42001, published in December 2023, is the first international standard for AI Management Systems (AIMS), providing a structured framework for governing, managing, and deploying AI systems responsibly

Bringing a MedTech product from prototype to mass production is a complex process that requires more than just technical feasibility. Regulatory compliance, cost-efficient design, and scalable manufacturing are critical to success. Without the right strategy, companies risk delays, high costs, and compliance failures. This guide covers the key steps to ensure a smooth transition from prototype to mass manufacturing while meeting FDA, MDR, and ISO 13485 standards.