Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China.
Cells. 2023 Jun 30;12(13):1755. doi: 10.3390/cells12131755.
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.
人工智能（AI）是计算机科学领域中一个快速发展的分支，涉及开发可以模拟人类智能的计算程序。特别是机器学习和深度学习模型已经实现了对数据中模式的识别和分组，从而开发出了已应用于血液病学各个领域的 AI 系统，包括数字病理学、α-地中海贫血患者筛查、细胞遗传学、免疫表型分析和测序。这些 AI 辅助方法在提高诊断准确性和效率、识别新的生物标志物和预测治疗结果方面显示出了潜力。然而，数据库有限、缺乏验证和标准化、系统误差和偏差等限制因素使得 AI 无法完全取代血液病学中的手动诊断。此外，AI 处理大量患者数据和个人信息可能会引发潜在的数据隐私问题，因此需要制定法规来评估 AI 系统，并解决临床 AI 系统中的伦理问题。尽管如此，随着持续的研究和开发，AI 有可能彻底改变血液病学领域并改善患者的治疗效果。然而，为了充分发挥这一潜力，必须解决和克服 AI 在血液病学中面临的挑战。
