ReviewJournal of primary care & community health
Harnessing Artificial Intelligence and Innovative Vaccines for Mpox Diagnosis and Control: A Comprehensive Narrative Review.
Review in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Public health surveillance and outbreak preparedness for mpox.Current opinion in HIV and AIDS · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe re-emergence of monkeypox (mpox) has triggered a global alert and galvanized efforts toward a scientific reappraisal of the disease.
aimThis study aims to provide a review of the use of Artificial Intelligence (AI) and novel vaccines in reducing the burden of mpox. METHODOLOGY: 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.
resultThe 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.
conclusionAI 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.