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

• How to build compliance into your development workflow so records come out as a by-product, not a pre-audit scramble
• How predetermined change control plans (PCCPs) let you pre-authorize software and AI updates, so you ship changes without a new submission every time
• How to right-size design controls and risk management to actual risk, not worst-case assumptions

• 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

• Addressing the greatest threats in a supplier management program
• Establishing trust and moving forward

• 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

• Discover how HIL and SIL testing are reshaping medical device verification by enabling continuous, simulation-driven
  validation across the software lifecycle.
• Gain practical insights into reducing development risk, accelerating time-to-market, and building regulatory-ready evidence
  through modern V&V practices

• Apply a two-level vertical slicing approach to decompose medical device software capabilities into end to end capabilities
• Identify architectural dependencies and foundational features early to reduce integration risk and structure milestone releases
• Enable flexible value driven release planning within regulated development and verification frameworks.

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.

• 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

Continue the conversation at our drinks reception, where you’ll have the opportunity to network
with software, quality, regulatory, cybersecurity and AI leaders from across the medical device
industry. Share practical experiences, benchmark approaches to compliant software development,
and build valuable connections with peers shaping the future of regulated medical software.