Clinical AI & Medical Device Regulation

What it covers

This practitioner-level programme equips regulatory affairs professionals and medical device teams with a comprehensive understanding of the EU Medical Device Regulation (MDR 2017/745), FDA Software as a Medical Device (SaMD) pathways, and IEC 62304 software lifecycle requirements as applied to AI/ML systems. Participants will work through real device classification exercises, quality management system (QMS) adaptation for ML models, and post-market surveillance planning for continuously learning algorithms. Delivery combines live expert-led sessions with structured case studies drawn from cleared and CE-marked AI devices, and culminates in a draft regulatory strategy document participants can apply immediately.

What you'll be able to do

• Classify an AI/ML-based software product under EU MDR and determine the correct conformity assessment route, including Notified Body involvement
• Draft a Predetermined Change Control Plan (PCCP) aligned with FDA guidance for a continuously learning SaMD product
• Identify and document the gaps between an existing ISO 13485 QMS and the additional requirements imposed by AI/ML model management
• Design a post-market surveillance and clinical follow-up plan that accounts for model drift and algorithm updates
• Map dual-regulation obligations where both the EU AI Act and MDR apply to the same product, identifying which authority takes precedence

Topics covered

• EU MDR 2017/745: classification rules and conformity assessment routes for AI-based devices
• FDA SaMD pathway: 510(k), De Novo, PMA, and the Predetermined Change Control Plan (PCCP)
• IEC 62304 software lifecycle applied to ML model training, validation, and retraining
• AI Act intersection with MDR: dual-regulation obligations for high-risk AI systems
• Clinical evaluation and PMCF planning for adaptive algorithms
• Post-market surveillance and vigilance reporting for ML-based devices
• Quality Management System (ISO 13485) adaptations for data-driven products
• Change management and version control strategies for continuously learning models

Delivery

Delivered as a blended programme over 4–6 weeks: approximately 50% live virtual instructor-led sessions (half-day blocks) and 50% asynchronous case study work and reading. In-person intensives can be arranged for groups of 8 or more. Each module includes annotated regulatory document templates (technical file sections, PMCF plans, PCCP templates). A dedicated Q&A clinic with a regulatory consultant is included in the final week. All materials are updated annually to reflect evolving MDCG guidance and FDA Digital Health Center of Excellence publications.

What makes it work

• Involve regulatory affairs, clinical, and ML engineering teams jointly in training to ensure shared vocabulary and aligned responsibility for compliance artefacts
• Apply learning immediately by working on a live or recent internal device project during case study sessions rather than hypothetical scenarios
• Establish internal change control procedures for model retraining before the product enters post-market phase, not after the first model update is needed
• Assign a designated regulatory owner for AI/ML product lines who maintains ongoing awareness of MDCG guidance updates and FDA Digital Health publications

Common mistakes

• Treating an AI/ML system as purely software and neglecting device classification rules, leading to incorrect conformity assessment routes and costly rework late in development
• Failing to define model change thresholds in advance, resulting in every algorithm update triggering a full re-submission rather than being covered by a PCCP or equivalent
• Underestimating post-market surveillance obligations for adaptive algorithms, particularly the need for real-world performance monitoring beyond traditional adverse event reporting
• Assuming CE marking under MDR automatically satisfies FDA SaMD requirements, and vice versa, without conducting a gap analysis between the two frameworks

Providers to consider

• TOPRA (The Organisation for Professionals in Regulatory Affairs)www.topra.org →
• RAPS (Regulatory Affairs Professionals Society)www.raps.org →
• TÜV SÜD Academy – Medical Device Regulation Trainingwww.tuvsud.com/en/services/academy →
• BSI Group – Medical Devices Trainingwww.bsigroup.com/en-GB/medical-devices/training →

Sources

• EU MDR 2017/745 – Official Journal of the European Union →
• FDA Guidance: Marketing Submission Recommendations for a Predetermined Change Control Plan for AI/ML-Enabled Device Software Functions →