With the continuous improvement of the accuracy of predictive models, the problems of algorithmic bias and the lack of fairness have become increasingly prominent. This not only may lead to significant differences in the accessibility of medical services and the effectiveness of diagnosis and treatment among different groups, but also may trigger medical risks such as misdiagnosis and missed diagnosis, treatment delays, as well as ethical disputes and legal issues under algorithmic discrimination, becoming the key reasons restricting the implementation of technology. At present, research on fairness has shifted from ethical and conceptual discussions to quantitative calculations, exploration of bias layer mechanisms, and exploration of technical mitigation strategies. This article, through a comprehensive analysis and discussion of the fairness issue of clinical prediction models, systematically sorts out the sources of model unfairness, fairness assessment and mitigation methods, as well as commonly used toolkits, and at the same time explores the future development trends. This study aims to sort out the current status of fairness studies on predictive models and promote the responsible application of artificial intelligence in the medical field.