Table 1_Artificial intelligence in vaccine research and development: an umbrella review.docx

BackgroundThe rapid development of COVID-19 vaccines highlighted the transformative potential of artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months. Nevertheless, the specific roles and effectiveness of AI in accelerating and enhancing vaccine research, development, distribution, and acceptance remain dispersed across various reviews, underscoring the need for a unified synthesis.
MethodsWe conducted an umbrella review to consolidate evidence on AI’s contributions to vaccine discovery, optimization, clinical testing, supply-chain logistics, and public acceptance. Five databases were systematically searched up to January 2025 for systematic, scoping, narrative, and rapid reviews, as well as meta-analyses explicitly focusing on AI in vaccine contexts. Quality assessments were performed using the ROBIS and AMSTAR 2 tools to evaluate risk of bias and methodological rigor.
ResultsAmong the 27 reviews, traditional machine learning approaches—random forests, support vector machines, gradient boosting, and logistic regression—dominated tasks from antigen discovery and epitope prediction to supply‑chain optimization. Deep learning architectures, including convolutional and recurrent neural networks, generative adversarial networks, and variational autoencoders, proved instrumental in multiepitope vaccine design and adaptive clinical trial simulations. AI‑driven multi‑omic integration accelerated epitope mapping, shrinking discovery timelines by months, while predictive analytics optimized manufacturing workflows and supply‑chain operations (including temperature‑controlled, “cold‑chain” logistics). Sentiment analysis and conversational AI tools demonstrated promising capabilities for real‑time monitoring of public attitudes and tailored communication to address vaccine hesitancy. Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.
Discussion and implicationsThese findings illustrate AI’s transformative potential across the vaccine lifecycle but underscore that translating promise into practice demands five targeted action areas: robust data governance and multi‑omics consortia to harmonize and share high‑quality datasets; comprehensive regulatory and ethical frameworks featuring transparent model explainability, standardized performance metrics, and interdisciplinary ethics committees for ongoing oversight; the adoption of adaptive trial designs and manufacturing simulations that enable real‑time safety monitoring and in silico process modeling; AI‑enhanced public engagement strategies—such as routinely audited chatbots, real‑time sentiment dashboards, and culturally tailored messaging—to mitigate vaccine hesitancy; and a concerted focus on global equity and pandemic preparedness through capacity building, digital infrastructure expansion, routine bias audits, and sustained funding in low‑resource settings.
ConclusionThis umbrella review confirms AI’s pivotal role in accelerating vaccine development, enhancing efficacy and safety, and bolstering public acceptance. Realizing these benefits requires not only investments in infrastructure and stakeholder engagement but also transparent model documentation, interdisciplinary ethics oversight, and routine algorithmic bias audits. Moreover, bridging the gap from in silico promise to real‑world impact demands large‑scale validation studies and methods that can accommodate heterogeneous evidence, ensuring AI‑driven innovations deliver equitable global health outcomes and reinforce pandemic preparedness.

研究背景
新冠疫苗的快速发展凸显了人工智能（AI）在现代疫苗学中的变革性潜力，将研发周期从数年缩短至数月。然而，AI在加速与优化疫苗研究、开发、分发及公众接受度方面的具体作用与效果，仍分散于各类综述文献中，这凸显了开展统一整合研究的必要性。

研究方法
本研究开展一项伞状综述（umbrella review），以整合AI在疫苗发现、优化、临床试验、供应链物流与公众接受度领域的贡献相关证据。我们系统检索了截至2025年1月的5个数据库，纳入聚焦疫苗领域AI应用的系统综述、范围综述、叙述性综述、快速综述及荟萃分析。采用ROBIS与AMSTAR 2工具开展质量评估，以评价偏倚风险与方法学严谨性。

研究结果
在纳入的27篇综述中，传统机器学习方法——包括随机森林、支持向量机、梯度提升与逻辑回归——主导了从抗原发现、表位预测到供应链优化的各类任务。深度学习架构，包括卷积神经网络、循环神经网络、生成对抗网络与变分自编码器，在多表位疫苗设计与适应性临床试验模拟中发挥了关键作用。AI驱动的多组学整合加速了表位图谱绘制，将发现周期缩短了数月；而预测分析则优化了生产流程与供应链运营（包括温控“冷链”物流）。情感分析与对话式AI工具展现出用于实时监测公众态度、定制化沟通以应对疫苗犹豫的良好潜力。不过，研究也发现了持续存在的挑战：数据异质性、算法偏见、监管框架缺失，以及透明度与公平性层面的伦理顾虑。

讨论与启示
本研究结果表明，AI在整个疫苗生命周期中具有变革性潜力，但同时也强调，要将其潜力转化为实际应用，需聚焦五大行动领域：建立健全的数据治理与多组学联盟，以协调共享高质量数据集；制定完善的监管与伦理框架，涵盖模型可解释性透明化、标准化性能指标，以及用于持续监督的跨学科伦理委员会；采用适应性试验设计与制造模拟技术，实现实时安全监测与虚拟过程建模；优化AI赋能的公众参与策略——例如经常规审计的聊天机器人、实时情感分析仪表盘，以及适配不同文化的信息传递——以缓解疫苗犹豫；通过能力建设、数字基础设施扩张、常规算法偏见审计，以及在资源匮乏地区提供持续资助，聚焦全球公平性与大流行防备工作。

结论
本项伞状综述证实，AI在加速疫苗研发、提升疫苗效力与安全性，以及增强公众接受度方面发挥着关键作用。要实现这些益处，不仅需要投入基础设施建设与利益相关方参与，还需实现模型文档透明化、跨学科伦理监督，以及常规开展算法偏见审计。此外，要弥合虚拟研发潜力与实际应用效果之间的差距，还需开展大规模验证研究，以及开发能够适配异质性证据的方法，确保AI驱动的创新能够带来公平的全球健康成果，并强化大流行防备能力。