Niu Jirui, Deng Ruotian, Dong Zipu, Yang Xue, Xing Zhaohui, Yu Yin, Kang Jian
Department of General Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Bio-Bank of Department of General Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Hum Vaccin Immunother. 2025 Dec;21(1):2501358. doi: 10.1080/21645515.2025.2501358. Epub 2025 May 16.
With the rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, their applications in the medical field have expanded significantly. Particularly in vaccine innovation, AI and ML have shown considerable potential. This article employs bibliometric analysis to examine the progress of AI and ML in vaccine innovation over recent years. By conducting literature retrieval, data extraction, and intelligent analysis through Web of Science, it provides more accurate and comprehensive insights into vaccine development and dosimetry. The rapid growth in research publications since 2012, particularly the geometric growth observed since 2017, underscores the increasing recognition of the potential of AI and ML to revolutionize vaccine development. However, despite the substantial benefits of AI and ML in vaccine innovation, challenges remain regarding data quality, algorithm reliability, and ethical considerations. As technology continues to advance and research deepens, AI and machine learning are anticipated to play an even more pivotal role in vaccine innovation. Notably, AI has the potential to accelerate vaccine development timelines, particularly in the context of emerging infectious diseases. By leveraging data-driven insights and predictive modeling, AI can streamline processes such as antigen discovery, clinical trial design, and risk assessment, thereby enabling faster responses to public health emergencies. This capability is especially critical for addressing sudden outbreaks of infectious diseases, where rapid deployment of effective vaccines can significantly mitigate global health risks.
随着人工智能（AI）和机器学习（ML）技术的迅速发展，它们在医学领域的应用显著扩大。特别是在疫苗创新方面，人工智能和机器学习已展现出巨大潜力。本文采用文献计量分析方法来审视近年来人工智能和机器学习在疫苗创新方面的进展。通过科学网进行文献检索、数据提取和智能分析，为疫苗研发和剂量测定提供了更准确、全面的见解。自2012年以来研究出版物的快速增长，尤其是自2017年以来呈现的几何级数增长，凸显了人们日益认识到人工智能和机器学习在变革疫苗研发方面的潜力。然而，尽管人工智能和机器学习在疫苗创新中带来了诸多益处，但在数据质量、算法可靠性和伦理考量方面仍存在挑战。随着技术不断进步和研究不断深入，预计人工智能和机器学习将在疫苗创新中发挥更关键的作用。值得注意的是，人工智能有潜力加快疫苗研发进程，特别是在新发传染病的背景下。通过利用数据驱动的见解和预测模型，人工智能可以简化抗原发现、临床试验设计和风险评估等流程，从而能够对突发公共卫生事件做出更快反应。这种能力对于应对传染病的突然爆发尤为关键，因为快速部署有效的疫苗可以显著降低全球健康风险。
