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Research into the use of artificial intelligence (AI) algorithms within the field of prognostics and health management (PHM), in particular for predicting the remaining useful life (RUL) of mechanical systems that are subject to condition monitoring, has gained widespread attention in recent years. It is important to establish confidence levels for RUL predictions, so as to aid operators as well as regulators in making informed decisions regarding maintenance and asset life-cycle planning. Over the past decade, many researchers have devised indicators or metrics for determining the performance of AI algorithms in RUL prediction. While most of the popularly used metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), etc. were adapted from other applications, some bespoke metrics are designed and intended specifically for use in PHM research. This study provides a synopsis of key performance indicators (KPIs) that are applied to AI-driven PHM technologies of mechanical systems. It presents details of the application scenarios, suitability of using a particular metric in different scenarios, the pros and cons of each metric, the trade-offs that may need to be made in choosing one metric over another, and some other factors that engineers should take into account when applying the metrics.