Gerald Rigdon
Fellow, Software Engineering Boston Scientific Corporation
I have over 35 years of experience in safety critical software driven real-time embedded systems spanning multiple diverse industries, Process Control Instrumentation, Burner Controls, Dynamically Stabilized Balancing Machines, and Medical Devices. I spent 11 years leading the software development in a small private industrial instrumentation company, 2 years owning burner control projects, 6 years wearing numerous hats in a renowned startup company, and presently 18 years in a senior staff technical leadership position for a large publicly owned medical device corporation. Hands-on in all stages of the software development process in dynamic safety/security critical environments with breakthrough technologies.
Seminars
- 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
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
- Go through what teams must do to verify, validate, and keep traceability when AI tools help write software
- How to manage tool reliability, version changes, and documentation so AI-generated code remains testable and compliant
- Practical governance, human oversight, and documentation practices that allow safe use of AI while meeting regulatory expectations
