AI Fundamentals for Medical Devices

Artificial intelligence is increasingly used in medical devices and medical device software to support prediction, classification, pattern recognition, automation, monitoring, diagnostics, therapy support, and clinical decision-making. These technologies can create significant opportunities for innovation, but they also introduce specific challenges related to data quality, bias, explainability, robustness, lifecycle control, clinical performance and regulatory compliance.

For medical device professionals, a basic understanding of AI concepts is becoming essential. Regulatory, quality, software, clinical, and risk management teams need a common language to understand how AI systems are developed, validated, maintained, monitored and controlled throughout the product lifecycle.

This training provides a foundational introduction to artificial intelligence in the context of medical devices. It is designed as a baseline module for participants who need to understand the terminology, principles, opportunities and challenges of AI-enabled medical devices before moving into more advanced topics such as regulatory compliance, technical documentation, software lifecycle processes, clinical evaluation, post-market surveillance and auditing.

Key Topics Covered:

·     Fundamental AI concepts relevant to medical devices, including machine learning, algorithms, models, training data, validation data, test data, inference and performance metrics

·     Types of AI approaches used in MedTech, including supervised learning, unsupervised learning, deep learning and adaptive or continuous learning systems

·     Basic AI system lifecycle: data collection, model development, training, validation, testing, deployment, monitoring, maintenance and change control

·     Typical applications of AI in medical devices, including diagnostics, image analysis, monitoring, prediction, triage, decision support and therapy support

·     Key challenges in clinical AI applications, including bias, explainability, data representativeness, robustness, generalizability, cybersecurity and human oversight

·     Introduction to the regulatory context for AI-enabled medical devices under Regulation (EU) 2017/745

·     Overview of risk-based thinking for AI-enabled medical devices, including intended purpose, clinical claims, patient impact and software-related risks

·     Foundation for further training modules on AI compliance, technical documentation, software lifecycle processes, clinical evaluation, post-market surveillance and auditing

·     Case examples and introductory exercises

Benefits:

·       Build a solid foundation in AI concepts and terminology relevant to medical devices

·       Understand how AI technologies are applied in medical device software and AI-enabled medical devices

·       Recognize the main technical, clinical and regulatory challenges associated with AI-based medical technologies

·       Gain initial insight into the regulatory principles governing AI-enabled medical devices under the EU MDR

·       Develop a common language for communication between regulatory, quality, software, clinical and risk management teams

·       Establish the foundation for advanced training on AI compliance, technical documentation, software lifecycle processes, clinical evaluation, post-market surveillance and auditing

Certificate: Participants receive a Certificate of Attendance of DQS MED GmbH

Target Audience: Professionals in regulatory affairs, quality management, risk management, clinical affairs, documentation, PMS, and auditing, engineers, software developers, systems engineers and product/project managers involved in AI-enabled medical devices. Teams seeking a common foundation and terminology for AI concepts in medical devices. Supports understanding of regulatory, clinical and quality considerations. Provides a basis for advanced topics such as AI risk management, validation, data governance and EU AI Act requirements.