  Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is
crucial to avoiding its spread. However, the similarities between common skin
disorders and mpox and the need for professional diagnosis unavoidably impaired
the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak.
To address the challenge, we proposed "Super Monitoring", a real-time
visualization technique employing artificial intelligence (AI) and Internet
technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly.
Concretely, AI-mediated "Super Monitoring" (mpox-AISM) integrates deep learning
models, data augmentation, self-supervised learning, and cloud services.
According to publicly accessible datasets, mpox-AISM's Precision, Recall,
Specificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and
96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six
like-mpox skin disorders, and normal skin. With the Internet and communication
terminal, mpox-AISM has the potential to perform real-time and accurate
diagnosis for earlier-stage mpox in real-world scenarios, thereby preventing
mpox outbreak.
