Randomized controlled trials (RCTs) are currently the gold standard for the treatment effect comparisons; however, it is sometimes not feasible to conduct an RCT due to ethical and economic reasons. In the absence of evidence for head-to-head RCT direct comparison, the indirect comparison technique is an effective and resource-saving alternative. Matching-adjusted indirect comparison (MAIC) is an attractive method in the field of population-adjusted indirect comparisons between two trials. It can adjust for between-trial imbalances in the distribution of observed covariates by weighting the available individual patient data of the studied intervention and then match the aggregated data of the controlled intervention. Subsequently, the treatment effect comparison can be evaluated through the post-matched population. Although MAIC is gaining increasing attention in clinical research, especially in the evaluation of new drugs, efforts are still largely required for knowledge dissemination in China. In this paper, we briefly introduced the concepts, research value and examples, and pros and cons of MAIC.