Artificial Intelligence and Hidradenitis Suppurativa- A Systematic Review of Diagnosis and Severity Assessment

Abstract

Background: Hidradenitis Suppurativa (HS) requires accurate and timely diagnosis and severity assessment to improve clinical management. However, this remains a challenge due to the lack of standardized clinical tools. Artificial Intelligence (AI) allows for the creation of tools that assist the management of HS, with the potential to improve the diagnostic and severity assessment process. Aim: This systematic review aimed to synthesize and evaluate the current applications of AI in the diagnosis and severity assessment process of HS. Methods: A systematic search was conducted across PubMed, Web of Science, SCOPUS, IEEE Xplore, and ACM Digital Library databases from inception to 13 November 2024. Results: Five articles met the inclusion criteria of the review. A study evaluated the diagnostic accuracy of an AI tool by image processing method. Two studies applied machine learning algorithms to differentiate lesions of HS from other entities. Two proof-of-concept studies explore the potential use of AI to automate the severity assessment of HS. Conclusions: These articles offer different perspectives on the potential applications of AI. This review supports the potential of AI-based tools to standardize and assist in HS's diagnosis and severity assessment process. However, further improvement and clinical validation of these tools are needed.