Managing Development Artifacts in AI for Healthcare
A structured approach to tracking datasets, models, and evaluations to ensure reproducibility, clarity, and alignment with regulatory expectations in healthcare AI.
Digital Notebook
This space gathers technical notes on computer vision and medical AI. It is intended as a working notebook to organize ideas, summarize research papers, and document applied work across both academic and industry settings.
* The views expressed here are personal and do not reflect any current or past professional affiliations.
** Entries are shared as evolving notes rather than finalized articles.
A structured approach to tracking datasets, models, and evaluations to ensure reproducibility, clarity, and alignment with regulatory expectations in healthcare AI.
An overview of FDA expectations when developing and evaluating AI models as medical devices.
A step-by-step guide to validating and verifying AI models, from development and testing to clinical evaluation and regulatory clearance.