JMIR Publications

ABSTRACT

Background:

The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management.

Objective:

This study aims to investigate the current research status in the utilization of AI for herpes zoster, offering a comprehensive synthesis of existing advancements.

Methods:

A systematic literature review was conducted following PRISMA guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research in November 2023. Inclusion criteria were: (1) research articles, (2) English language, (3) actual AI applications, and (4) focus on herpes zoster. Exclusion criteria comprised non-research articles, non-English papers, and studies only mentioning AI without application. Two independent reviewers screened the studies, with a third resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types, algorithms, data sources, data types, and clinical applications in diagnosis, treatment, and management.

Results:

Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (57.7%), followed by public databases (23.1%) and internet data (19.2%). Medical images (34.6%) and electronic medical records (26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly Multilayer Perceptron and Convolutional Neural Networks being the most frequently used algorithms. AI applications were analyzed across three domains: (i) Diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (ii) Treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (iii) Management, where AI has facilitated case identification, epidemiological research, healthcare burden assessment, and risk factor exploration for post-herpetic neuralgia and other complications.

Conclusions:

Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving field. AI has significantly advanced herpes zoster research by enhancing diagnostic accuracy, predicting treatment outcomes, and optimizing disease management. However, several limitations exist, including potential omissions from excluding databases like Embase and Scopus, language bias due to the inclusion of only English publications, and the risk of subjective bias in study selection. More broader studies and continuous updates are needed to fully capture the scope of AI applications in herpes zoster in the future. Clinical Trial: Not applicable