ML in life sciences: navigating regulatory expectations

Regulators increasingly expect clarity on data, model versioning, and validation. We summarize key expectations and how to build an ML development process that supports submission. Topics include traceability, explainability, and the role of independent validation. We also cover practical documentation and MLOps practices that make audits straightforward.