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人工智能和机器学习方法在非洲传染病疫苗研发生态系统中的可持续整合。

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem.

作者信息

Hare Jonathan, Nielsen Morten, Kiragga Agnes, Ochiel Daniel

机构信息

Biolife Research Limited, Nairobi, Kenya.

Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.

出版信息

Front Pharmacol. 2024 Dec 17;15:1499079. doi: 10.3389/fphar.2024.1499079. eCollection 2024.

Abstract

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.

摘要

人工智能和机器学习(AI/ML)技术,包括反向疫苗学和预测模型,已被应用于开发针对COVID-19、艾滋病毒和肝炎的候选疫苗,简化了从发现到部署的疫苗开发生命周期。人工智能和机器学习技术在改善健康干预措施方面的应用,包括药物发现和临床开发,正在非洲各地扩展,特别是在南非、肯尼亚和尼日利亚。然而,需要进一步采取举措,在整个非洲大陆扩展人工智能/机器学习能力,以确保发展一个可持续的生态系统,包括加强必要的知识库、促进利益相关者之间的合作、确保健全的监管和道德框架以及对必要基础设施的投资。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8785/11685015/4c663205e4a5/fphar-15-1499079-g001.jpg

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