HealthTech Regulatory Compliance Quiz

The process of evaluating and mitigating potential risks associated with a medical device throughout its entire lifecycle, from design to manufacturing to use.

A document that provides detailed information about a medical device, including its intended use, indications, contraindications, warnings, precautions, and adverse effects.

The process of ensuring that a medical device meets its intended performance and safety requirements.

The legal framework for medical devices in the European Union.

The legal framework for medical devices in the United States.

The process of developing and testing a medical device to ensure it meets regulatory requirements and is safe and effective for its intended use.

The process of ensuring that a medical device meets its intended performance and safety requirements, especially in the context of clinical use.

A type of medical device regulation that focuses specifically on in vitro diagnostic (IVD) devices.

A legal right granted to an inventor for an invention, allowing the inventor to exclude others from making, using, or selling the invention.

The subject matter that can be protected by a patent, including processes, machines, manufactures, and compositions of matter.

An invention must be novel, non-obvious, and useful to be patentable.

The period between an invention's creation and the filing of a patent application.

The official process of applying for and obtaining a patent, starting with a patent application and ending with a patent grant.

A patent gives the patentee the exclusive right to exploit the invention for a specified period, usually 20 years.

ETH Zurich owns inventions created by its employees or students during their employment or studies.

A process where a patent holder grants another party the right to use the invention under certain conditions.

A mindset that considers regulatory compliance throughout the entire lifecycle of a health technology product, from ideation to market launch and beyond.

Proactive compliance means ensuring your product meets regulations right from the start, instead of scrambling to fix things at the end.

Regulatory compliance should be viewed as a strategic tool that strengthens your product's credibility and earns the trust of healthcare providers and patients.

Regulatory thinking anticipates future regulatory shifts, emerging technologies, and evolving market demands, ensuring product viability over time.

Integrating compliance early in the development phase minimizes the risk of costly delays caused by overlooked regulations.

Proactive compliance helps avoid expensive recalls, rework, or fines caused by non-compliance, saving you money in the long run.

Compliance is a key factor in gaining the trust of healthcare providers, patients, and payers.

Regulatory compliance is not just about meeting the minimum standards, but about demonstrating a commitment to quality and safety.

The ability of an AI system to explain its decision-making process and provide insights into how it arrived at a particular outcome.

Data used to train AI models should represent the diverse population the device will be used on. Lack of diversity can lead to biased outcomes.

Regulations that govern AI development and usage in healthcare differ between countries and regions. This can lead to different testing and approval processes.

The process of identifying and mitigating bias in an AI model. This is crucially important for ensuring fairness and accuracy.

Training AI models with data that represents the population who will actually use the device is key to avoiding biases and ensuring accurate results.

AI models that use deep learning often have complex internal processes that are difficult to understand. This is known as the 'black box' problem.

AI development and regulation is a global issue, with different approaches in different regions.

AI systems should be trustworthy and transparent, especially in healthcare where decisions can impact patients' lives.

Ensuring AI systems are free from bias and transparent.

The European Union's law for regulating high-risk AI systems, emphasizing transparency and bias mitigation.

The FDA's guidelines for developing, monitoring, and ensuring fairness in AI medical devices.

Human involvement is essential for AI, especially in healthcare, enabling medical professionals to review and override decisions made by AI.

Continually monitoring an AI system after its release is crucial to identify and address potential biases that may emerge from new data.

Brazil's regulatory body for medical devices and health products, with a classification system similar to the EU.

Canada's regulatory body for medical devices, using a classification system similar to the FDA and MDR.

Australia's regulatory body for medical devices and pharmaceuticals, using a risk-based classification system for approval.

Periodic assessments conducted by notified bodies or certification bodies to ensure ongoing compliance with ISO 13485 standards.

A comprehensive process that involves systematically collecting, analyzing, and evaluating data about the safety and performance of medical devices after they've been released to the market.

Software specifically designed for use in medical settings, such as diagnosing medical conditions.

A systematic approach to identifying, analyzing, and evaluating potential risks associated with medical devices throughout their lifecycle.

The process of demonstrating that a medical device meets its intended performance requirements under real-world conditions and satisfies safety standards.

The process of systematically evaluating a medical device against its design specifications and intended use to ensure that it meets specific performance requirements.