人工智能（AI）及其在外周血膜中血细胞分类中的应用是血液学中不断发展的领域。我们对人工智能和外周血膜的文献进行了快速回顾，评估所研究的条件，图像数据集，机器学习模型，训练集大小，测试集的大小和准确性。总共确定了283项研究，涵盖6大类：疟疾(n=95)，白血病（n=81），白细胞（n=72），混合(n=25)，红细胞（n=15）或骨髓增生异常综合征（MDS）（n=1）。这些出版物在各种研究中显示出很高的自我报告平均准确率（疟疾为95.5％，白血病占96.0%，94.4%的白细胞，混合研究为95.2％，红细胞为91.2％），总体平均准确率为95.1%。尽管准确度很高,这些人工智能训练模型在现实世界中的转化使用面临的挑战包括需要经过充分验证的多中心数据，数据标准化，以及不太常见的细胞类型和非疟疾血液传播寄生虫的研究。
Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid
review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.
