Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in...

Area of Science:

• Dermatology
• Medical Informatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) integration in patch testing for allergic contact dermatitis (ACD) offers potential for standardized diagnoses and improved accuracy.
• However, significant challenges and limitations impede its clinical implementation.

Purpose of the Study:

• To review current AI applications in patch testing for ACD.
• To identify challenges and limitations in AI implementation.
• To propose future research directions for AI in dermatological diagnostics.

Main Methods:

• A narrative review of studies using AI in human patch testing was conducted.
• PubMed was searched in August 2024, with inclusion criteria for English, original research involving human participants.
• Data synthesis focused on study design, AI performance, and clinical applicability.

Main Results:

• Ten out of 94 reviewed articles met inclusion criteria.
• Convolutional neural networks (CNNs) showed high accuracy (90.1%–99.5%) in image analysis.
• Other AI models were used for risk prediction and biomarker discovery, with limitations including small sample sizes and non-standardized protocols.

Conclusions:

• AI holds significant potential to enhance ACD diagnostic accuracy and standardize patch testing interpretation.
• Realizing AI's full potential requires standardized imaging protocols, larger diverse datasets, and robust regulatory frameworks.