JMIR Publications

ABSTRACT

Background:

The integration of artificial intelligence (AI) into patch testing for allergic contact dermatitis (ACD) holds the potential to standardize diagnoses, reduce inter-observer variability, and improve overall diagnostic accuracy. However, the challenges faced by the field and the limitations hindering clinical implementation have not been thoroughly explored.

Objective:

This narrative review aims to examine the current applications of AI in patch testing, identify challenges, and propose future directions for their use in dermatology.

Methods:

PubMed was searched in August 2024 to identify studies involving human participants undergoing patch testing with AI or ML interventions. Exclusion criteria were non-English articles and non-original research. Data were synthesized to assess study design, performance, and potential for clinical application.

Results:

Ten out of 94 reviewed articles met the inclusion criteria. The majority utilized convolutional neural networks (CNNs) for image analysis, with accuracy rates ranging from 90.1% to 99.5%. Other AI models, such as Gradient Boosting and Random Forest, were used for risk prediction and biomarker discovery. Key limitations included limited sample sizes, variability in image capture protocols, and lack of standardized reporting on skin types.

Conclusions:

AI has significant potential to enhance diagnostic accuracy and standardize patch test interpretation with the potential to help expand access to patch testing. However, standardized imaging protocols, larger and more diverse datasets, and improved regulatory frameworks are necessary to realize the full potential of AI in patch testing.