Harnessing Artificial Intelligence and Innovative Vaccines for Mpox Diagnosis and Control: A Comprehensive Narrative Review.

Abstract

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.