OnRAMP for Regulating Artificial Intelligence in Medical Products

Abstract

Medical artificial intelligence (AI) involves the application of machine learning (ML) algorithms to biomedical datasets to improve medical practices. Products incorporating medical AI require certification before deployment in most jurisdictions. To date, clear pathways for regulating medical AI are still under development. Below the level of formal pathways lies the actual practice of developing a medical AI solution. This Perspective proposes best practice guidelines for development compatible with the production of a regulatory package which, regardless of the formal regulatory path, will form a core component of a certification process. The approach is predicated on a statistical risk perspective, typical of medical device regulators, and a deep understanding of ML methodologies. These guidelines will allow all parties to communicate more clearly in the development of a common good machine learning practice (GMLP), and thus lead to the enhanced development of both medical AI products and regulations.

Keywords

• artificial intelligence
• biostatistics
• medical applications
• methodologies
• regulatory guideline