The integration of Artificial Intelligence (AI) in Medical Device Software (MDSW) can bring opportunities to enhance clinical workflow. However, this has also brought challenges to manufacturers, of which additional considerations during the development process must be contemplated. As an initial step to address the development of AI-enabled Medical Device (AIeMD), this review paper aims to identify relevant AI Standards for an initial understanding and propose an alignment of the development life cycle (DLC) of MDSW and AI systems. Standards are resources to harmonise best practices, some of which are also accepted to address aspects of MDSW. In the AI domain, standardisation work is a key ongoing process to ensure the responsible development of AI systems. ISO/IEC 5338 provides a life cycle framework for AI systems; however, identifying related standards is essential to consider consistent use of processes, stages, activities, and terminologies. As numerous AI Standards have been published in recent years, the most relevant AI Life Cycle-related standards are identified through the AI Standards Hub (AIHub). A set of criteria is established to select standards that cover the DLC of AI systems, including standards associated with the data management life cycle. The search on AIHub brought 33 AI-specific standards, most of which were issued by ISO/IEC. After cross-validation, the standards were inspected to identify additional standards not included in the AIHub results, resulting in four more being added to the list. After data selection, eleven AI life cycle-related standards were chosen towards implementing a framework for the DLC of AIeMD.