With the advent of artificial intelligence/machine learning (AI/ML) and its strength in faster and more accurate disease detection and diagnosis, it is inspiring that more timely and widespread adoption of decision-making assistant (DMA) software as a medical device (DMA-SaMD) would benefit improving health for human beings. However, that does not mean the AI/ML-based DMA-SaMD for decision making is ready for the clinic, AI/ML technology can only be used with complete confidence if it has been quality controlled through a rigorous evaluation in a standardized way. The performance and usability shall be assessed under a reliable and rigorous evaluation with a robust method to substantiate AI/ML-based DMA-SaMD quality. This Recommendation provides a requirement framework for the quality assessment with a perspective of lifecycle management for AI/ML-based DMA-SaMD. It describes the quality assessment principles and process in the life cycle of AI/ML-based DMA-SaMD, including requirement analysis, data collection, algorithm design, verification and validation, change control and other stages when using AI/ML technology to assist medical staff in making clinical decisions by providing suggestions on diagnostic and treatment activities.