Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the medical device industry.
From diagnostic imaging and digital pathology to implantable devices, remote monitoring applications, and personalized treatment recommendations, much of the future of healthcare relies on software that can learn, adapt, and support complex decision-making. Unlike traditional deterministic software, where outputs are predictable and static, AI/ML systems generate dynamic outcomes based on evolving algorithms and real-world data.
This significant shift introduces new regulatory, safety, ethical, and lifecycle control challenges that device manufacturers must understand. This webinar provides a comprehensive and practical exploration of these challenges and offers a roadmap for compliance, development, and risk control.
The session begins by defining how AI/ML differs from conventional medical device software and why the technology demands a fundamentally different approach to validation, oversight, and change control. Participants learn how regulators such as FDA and the European Union view AI/ML technologies not as black-box tools but as medical products requiring full lifecycle assurance, algorithm transparency, clinical reliability, and post-market performance control.
The webinar explains emerging regulatory frameworks and standards, including FDA's Total Product Lifecycle approach and the Predetermined Change Control Plan (PCCP), as well as EU MDR requirements, ISO 13485, IEC 62304, ISO 14971, and emerging AI-specific standards such as ISO/IEC 23894.
Risk management forms a central part of this training. Attendees learn to identify and mitigate unique AI/ML hazards such as data bias, model drift, cybersecurity vulnerabilities, adversarial manipulation, and unequal model performance across different patient populations.
The importance of data quality is emphasized, including training dataset selection, validation controls, real-world performance monitoring, and governance for data integrity. The webinar explains how bias, poor sampling, data contamination, or unrepresentative patient populations can directly impact the clinical safety and effectiveness of the device.
Participants also receive practical guidance on design controls, testing, validation, and clinical performance evaluation of AI/ML models. The session explores software verification and validation methods tailored to AI, including scenario-based testing, edge case evaluation, robustness testing, post-market learning rules, and confidence threshold management.
The differences between static (locked) algorithms and adaptive learning systems are explained, with step-by-step insights into how real-world model updates must be planned, documented, and controlled.
A critical portion of the training addresses cybersecurity and ethical obligations. Because AI/ML models rely heavily on data integrity and connectivity, cybersecurity weaknesses can influence predictions or allow malicious tampering with models. Ethical considerations such as explainability, transparency, and prevention of discriminatory outcomes are presented as core regulatory requirements, not optional business values. Participants learn how to document and communicate these controls to demonstrate regulatory compliance, patient safety, and trustworthiness.
By the end of this webinar, attendees will understand how to develop, validate, document, and maintain AI/ML medical devices in a way that meets global regulatory expectations and protects patients through robust lifecycle control, transparency, and proactive risk management.
Why should you Attend:
AI and Machine Learning are rapidly reshaping the medical device industry, and professionals who understand how to develop, validate, and maintain these technologies responsibly will be at the center of the next wave of healthcare innovation. However, unlike traditional software, AI/ML models introduce unpredictable outputs, evolving behaviors, and significant ethical and regulatory challenges. This training helps participants navigate these complexities by providing practical, real-world guidance on how to build compliant, safe, and trustworthy AI-enabled devices.
Students who take this webinar will gain the critical knowledge needed to align their work with FDA, EU MDR, and global standards while understanding how regulators expect algorithms to be controlled, monitored, and updated especially when models continue to learn after deployment. They will learn how to address unique AI risks such as dataset bias, cybersecurity threats to algorithms, data governance weaknesses, and model drift that can compromise clinical safety.
This training also helps attendees understand how to design and validate AI/ML systems so they perform reliably across diverse patient populations. Participants will learn how to plan clinical performance evaluation, implement human oversight where needed, and document transparency, explainability, and ethical safeguards that regulators increasingly require.
Whether a participant is involved in design, risk management, clinical evaluation, software development, regulatory affairs, or quality assurance, this knowledge will become essential in the years ahead. Taking this course will not only strengthen professional expertise but also position individuals as leaders in one of the most transformative areas of modern medical technology.
Areas Covered in the Session:
