How Does AI Support Skin Allergy Testing?

By Rasit Dinc

Introduction

Skin allergies are a common ailment, affecting a significant portion of the global population. The diagnostic process for these allergies often involves skin prick tests or patch tests, which, while effective, can be time-consuming, subjective, and require multiple clinic visits. Traditional methods rely heavily on the visual interpretation of a clinician, which can lead to variability in results. However, the integration of artificial intelligence (AI) into dermatology is paving the way for a new era of diagnostics, promising to make skin allergy testing more accurate, efficient, and accessible than ever before [1].

The Convergence of AI and Dermatology

The application of AI in dermatology is not a new concept. For years, researchers have been exploring the potential of machine learning algorithms to analyze medical images and assist in the diagnosis of various skin conditions, from melanoma to psoriasis [2]. These AI-powered systems can be trained on vast datasets of images to recognize patterns and anomalies that may be imperceptible to the human eye. This capability has proven invaluable in improving diagnostic accuracy and streamlining workflows in busy dermatology practices. The extension of this technology to the realm of allergy testing is a natural and exciting progression.

Revolutionizing Skin Allergy Testing with AI

Recent advancements have led to the development of innovative AI-powered solutions specifically designed to enhance skin allergy testing. One such example is an application developed by the Mayo Clinic, which allows patients to perform patch testing from the comfort of their own homes. The process is simple yet effective: patients apply the test patches themselves, capture images of their skin using the app at predetermined times, and an AI algorithm analyzes the results. A healthcare professional then validates the AI's assessment, ensuring accuracy and providing a definitive diagnosis [1]. This approach not only eliminates the need for multiple clinic visits but also has the potential to expand access to testing for individuals in remote or underserved areas.

Another significant development is the Skin Prick Automated Test (SPAT), which utilizes an automated device to perform multiple skin prick tests simultaneously with a controlled amount of allergen and prick force. After a set time, images of the forearm are captured and analyzed by an AI-assisted readout method. A study published in Nature Communications demonstrated the high accuracy of this method, with the AI's measurements showing a strong correlation with those of treating physicians. The study reported a specificity of 98.4% and a sensitivity of 85.0% for the AI algorithm [2]. In a test cohort, physicians only needed to adjust 5.8% of the AI's measurements, and these adjustments led to a change in the final interpretation in a mere 0.5% of cases. These findings underscore the potential of AI to significantly reduce inter- and intra-observer variability and decrease the time required for readout, thereby improving the overall standardization and efficiency of the skin prick test process.

<br> <br>

Challenges and the Path Forward

Despite the promising results, the application of AI in skin allergy testing is not without its challenges. The accuracy of AI algorithms can be affected by various factors, including the presence of scars, hyperpigmentation, dark skin tones, and even hairy forearms [2]. It is crucial to train these models on diverse datasets that are representative of the entire population to ensure equitable and accurate performance across all skin types. Further research and development are needed to refine these algorithms and address these limitations.

The future of AI in allergy testing is bright. As the technology continues to evolve, we can expect to see even more sophisticated and accurate diagnostic tools. The integration of AI with other technologies, such as teledermatology platforms, could further enhance patient care by providing remote consultations and follow-ups. The ultimate goal is to create a seamless and patient-centric diagnostic experience that is both highly accurate and easily accessible.

Conclusion

Artificial intelligence is poised to revolutionize the field of skin allergy testing. By automating the analysis of test results, AI-powered solutions can improve accuracy, reduce variability, and increase efficiency. While challenges remain, the ongoing research and development in this area are paving the way for a future where diagnosing skin allergies is faster, more reliable, and more accessible for everyone. The continued collaboration between clinicians, researchers, and AI developers will be essential in realizing the full potential of this transformative technology.

References

[1] Mayo Clinic News Network. (2025, March 12). Tomorrow's Cure: How AI enhances skin allergy testing. Retrieved from https://newsnetwork.mayoclinic.org/discussion/tomorrows-cure-how-ai-enhances-skin-allergy-testing/

[2] Seys, S. F., Hox, V., Chaker, A. M., De Greve, G., Lemmens, W., Poirrier, A. L., ... & Van Gerven, L. (2025). Artificial Intelligence (AI)-assisted readout method for the evaluation of skin prick automated test results. Nature Communications, 16(1), 8637. https://doi.org/10.1038/s41467-025-64334-w