Day One

• Why traditional regulatory and quality models are struggling with AI and rapid software change
• How leading companies are restructuring teams across software, quality, regulatory, and cybersecurity
• The shift from “submission-focused” thinking to lifecycle accountability

• Recognize risks introduced by model drift, data drift, and real-world performance degradation
• Identify and manage governance challenges with rapidly iterating AI versions and generative tools
• Learn monitoring strategies for “unlocked” or adaptive models in the post-market phase
• What performance, safety, and usage signals regulators increasingly expect teams to track

• Explore using AI for code generation, unit tests, peer review, and documentation drafting
• Identify hidden risks: hallucinated logic, unverifiable outputs, and traceability gaps
• Learn Verification strategies when AI contributes to regulated development artifacts
• Defining where human oversight and accountability must remain in AI-assisted workflows

As the Medical Device Community unites, this session will provide valuable networking time

with your peers, enabling you to forge new and lasting connections and gain unfiltered insight to

discuss your challenges.

• A high level look at how AI/ML is embedded in biotech and oncology solutions and how current approaches differ from earlier ML
• What modern AI tools support engineering teams during software development, from code assistance to design and implementation, in regulated medical device environments
• Emerging uses of AI in testing and validation, including intelligent or agent based methods for evaluating software behavior and their impact on quality and compliance

• Understand why AUC, sensitivity, and specificity are insufficient to demonstrate clinical safety
• The “no gold standard” problem: disagreement among clinicians as a validation challenge
• Identifying bias, edge cases, and underrepresented populations in training/testing data
• Integrating AI validation with risk management, V&V, and regulatory evidence expectations

• Exploring how to design a supportive, agent-driven security ecosystem that connects threat modeling, platform controls, and real-time security operations into a unified framework
• Moving beyond static, submission-driven threat models toward continuous validation, monitoring, and iterative risk reassessment across the software lifecycle
• Lessons learned from operationalizing this approach to create a more reactive, evidence-backed security posture that strengthens compliance while improving realworld resilience

• Understand regulatory expectations for SOUP and open-source components
• Learn how to build and maintain regulator-ready software bills of materials
• Explore risk scoring and vulnerability monitoring strategies
• Leave with a scalable approach to third-party software governance

This is an informal session to help you connect with your peers in a relaxed atmosphere to continue forging new and beneficial relationships. With an audience of medical tech experts eager to hear the latest innovations in software developments space, you will have the opportunity to display a poster presenting your own research.

• Identify where misalignment causes delays and compliance gaps
• Learn how to translate design controls into engineering-friendly processes
• Explore shared ownership models for evidence and risk
• Leave with tactics to improve collaboration without slowing innovation

• Real-world walkthrough followed by audience Q&A exploring how approaches scale across different company sizes
• See how enterprise-grade processes were simplified without losing rigor
• Learn where smaller teams often over-engineer compliance
• Understand tool and process trade-offs in lean environments
• Leave with ideas to right-size your own quality and engineering systems

This dynamic fishbowl debate brings engineers, clinicians, and regulatory leaders into a rotating inner‑circle conversation, creating a rare, real‑time exchange between those designing AI systems, those deploying them, and those overseeing their safety. With audience members stepping into the discussion throughout, the session becomes a living model of the very human‑machine interaction it examines.

• Discuss how assistive, decision‑support, and autonomous AI shift risk, validation, and oversight, enabling attendees to match autonomy levels to the appropriate regulatory and safety strategy
• Examine where AI improves consistency and where it introduces over‑reliance or hidden errors, giving participants clearer judgment on when human review adds safety value
• Explore how claims, labelling, and clinical context constrain autonomy, helping attendees align design decisions with regulatory positioning
• Break down models for “human in the loop,” “on the loop,” and supervisory control guiding teams to design workflows, interfaces, and monitoring that meet regulator and clinician expectations