Ernest-Okonofua Excel Onajite, Zubairu Zainab Abdullahi, Oduoye Malik Olatunde, Tariq Maryam, Muhammad Syed, Siddiqua Zainab, Vuyyuru Monica, Wafula Benjamin, Akhtar Riaz, Fasasi Abdulbasit, Ubechu Samuel Chinonso, Olayinka Bakare Sikiru
University of South Wales, UK.
Ahmadu Bello University, Zaria, Kaduna State, Nigeria.
J Prim Care Community Health. 2025 Jan-Dec;16:21501319251357701. doi: 10.1177/21501319251357701. Epub 2025 Jul 23.
The re-emergence of monkeypox (mpox) has triggered a global alert and galvanized efforts toward a scientific reappraisal of the disease.
This study aims to provide a review of the use of Artificial Intelligence (AI) and novel vaccines in reducing the burden of mpox.
A narrative review was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines through electronic databases including PubMed, Google Scholar, ResearchGate, and Web of Science (WOS), using keywords such as Mpox, machine learning, deep learning, diagnosis and novel vaccines between the last 5 years (2019-2024). Included studies comprised clinical trials, cross-sectional studies, systematic reviews, meta-analyses, case reports, and case series written in the English language.
The diagnosis of mpox has been greatly aided by the use of AI, including machine learning (ML), deep learning (DL), artificial neural network (ANN), convolutional neural network (CNN), and transfer learning (TL). AI can help with the development of novel diagnostic tests, increasing the accuracy and speed of mpox detection, which is critical for successful epidemic management. Reported model accuracies for mpox lesion classification and disease trend prediction ranged from 83 to 99.8%, underscoring the high potential of AI-based tools in this field. Vaccines developed against smallpox, such as ACAM2000, LC16m8, and MVA-BN (JYNNEOS), have shown partial efficacy in preventing mpox transmission, providing cross-protection against mpox due to the genetic similarity between the 2 viruses.
AI has proven to be significant in mpox detection, treatment, and prevention. Future directions should be focused on healthcare professionals to establish the validity and reliability of the models, a measure of the algorithm's robustness, and the continuous auditing of AI systems.
猴痘（mpox）的再度出现引发了全球警报，并促使各方努力对该疾病进行科学重新评估。
本研究旨在综述人工智能（AI）和新型疫苗在减轻猴痘负担方面的应用。
根据系统评价和Meta分析的首选报告项目（PRISMA）指南，通过电子数据库进行叙述性综述，这些数据库包括PubMed、谷歌学术、ResearchGate和科学网（WOS），使用过去5年（2019 - 2024年）间的关键词，如猴痘、机器学习、深度学习、诊断和新型疫苗。纳入的研究包括以英文撰写的临床试验、横断面研究、系统评价、Meta分析、病例报告和病例系列。
AI的应用极大地辅助了猴痘的诊断，包括机器学习（ML）、深度学习（DL）、人工神经网络（ANN）、卷积神经网络（CNN）和迁移学习（TL）。AI有助于开发新型诊断测试，提高猴痘检测的准确性和速度，这对成功的疫情管理至关重要。报道的基于AI的工具在猴痘病变分类和疾病趋势预测方面的模型准确率在83%至99.8%之间，凸显了该领域基于AI的工具的巨大潜力。针对天花研发的疫苗，如ACAM2000、LC16m8和MVA - BN（JYNNEOS），在预防猴痘传播方面显示出部分效果，由于这两种病毒的基因相似性，能提供针对猴痘的交叉保护。
AI已被证明在猴痘的检测、治疗和预防中具有重要意义。未来的方向应聚焦于医疗保健专业人员，以确定模型的有效性和可靠性、衡量算法稳健性的指标，以及对AI系统的持续审核。
