Harnessing artificial intelligence for advancing early diagnosis in hidradenitis suppurativa

Abstract

This perspective delves into the integration of artificial intelligence (AI) to enhance early diagnosis in hidradenitis suppurativa (HS). Despite significantly impacting Quality of Life, HS presents diagnostic challenges leading to treatment delays. We present a viewpoint on AI-powered clinical decision support system designed for HS, emphasizing the transformative potential of AI in dermatology. HS diagnosis, primarily reliant on clinical evaluation and visual inspection, often results in late-stage identification with substantial tissue damage. The incorporation of AI, utilizing machine learning and deep learning algorithms, addresses this challenge by excelling in image analysis. AI adeptly recognizes subtle patterns in skin lesions, providing objective and standardized analyses to mitigate subjectivity in traditional diagnostic approaches. The AI integration encompasses diverse datasets, including clinical records, images, biochemical and immunological data and OMICs data. AI algorithms enable nuanced comprehension, allowing for precise and customized diagnoses. We underscore AI's potential for continuous learning and adaptation, refining recommendations based on evolving data. Challenges in AI integration, such as data privacy, algorithm bias, and interpretability, are addressed, emphasizing the ethical considerations of responsible AI deployment, including transparency, human oversight, and striking a balance between automation and human intervention. From the dermatologists' standpoint, we illustrate how AI enhances diagnostic accuracy, treatment planning, and long-term follow-up in HS management. Dermatologists leverage AI to analyze clinical records, dermatological images, and various data types, facilitating a proactive and personalized approach. AI's dynamic nature supports continuous learning, refining diagnostic and treatment strategies, ultimately reshaping standards of care in dermatology.