OnRAMP for Regulating AI in Medical Products
This work addresses the problem of regulatory uncertainty for developers and regulators in the medical AI domain, though it is incremental as it builds on existing statistical risk perspectives without introducing new technical methods.
The paper tackles the lack of clear regulatory pathways for medical AI products by proposing best practice guidelines for development that align with regulatory requirements, aiming to facilitate certification and enhance communication among stakeholders.
Medical Artificial Intelligence (AI) involves the application of machine learning algorithms to biomedical datasets in order 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 machine learning 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.