The integration of software into medical devices has profoundly transformed the healthcare landscape, enabling more precise diagnostics, personalized treatment plans, and remote patient monitoring. The rapid pace of software development - especially with emerging technologies like artificial intelligence (AI) and machine learning (ML) - demands specialized regulatory frameworks to ensure patient safety, clinical effectiveness, and product reliability. Software as a Medical Device (SaMD) refers to software intended for medical purposes that operates independently of a physical medical device. This article traces the historical development of regulatory approaches, deconstructs existing frameworks, analyzes key challenges and influencing factors, presents case studies, and anticipates future trends. A narrative literature search was conducted using PubMed, Google Scholar, FDA.gov, EUR-Lex and IMDRF.org, covering materials published between January 2013 and May 2025. The Total Product Lifecycle (TPLC) framework is essential for managing the evolving performance and risks associated with AI/ML-based SaMD. AI/ML-powered SaMD is not merely transforming healthcare delivery, it is challenging and redefining the foundational assumptions of medical regulation. The regulatory systems must evolve ensuring transparency, maintaining public trust, addressing algorithmic bias, and promoting equitable access to the benefits of these advanced technologies. Innovation must be guided by a framework rooted in safety, integrity, and equity.
Shah et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: