AIaMD requires the same technical documentation components used for other medical devices - intended purpose, risk management, software lifecycle processes, usability engineering, cybersecurity, clinical evaluation, post-market surveillance etc. The challenge is not that these elements are unfamiliar, but that AIaMD submissions often reuse “standard” approaches without adapting them to AI-specific safety and performance behaviours. The result is avoidable review questions, rework, and delays.

This session shares a Notified Body perspective on what most commonly prevents teams from “getting it right first time,” and how to strengthen the technical documentation so it forms a coherent, traceable safety and performance argument. We will highlight frequent issues such as: performance claims that are not converted into testable requirements and acceptance criteria; risk management that does not fully capture AI-relevant hazard pathways (data representativeness and quality, performance variability across sites and sub-populations); risk controls that are described but not demonstrated through clear implementation evidence and verification/validation; clinical evaluation that does not adequately justify performance in the intended context of use or articulate limitations, uncertainty, and residual risk; and post-market surveillance plans that are not explicitly tied to identified risks or do not define practical signals, thresholds, and actions to manage real-world performance and controlled change.

Attendees will leave with a practical set of reviewer-style questions and a lightweight traceability approach to connect claims → requirements → risks → controls → evidence → post-market commitments - helping teams create AIaMD technical documentation that is robust, reviewable, and maintainable across iterative software and model lifecycles.