Artificial Intelligence in Infectious Skin Disease

Background: Infective and infectious dermatological diseases, which range from minor diseases like impetigo to serious diseases like deep fungal infections, pose significant public health issues. Given the growth of drug‐resistant microorganisms, it is critical to investigate novel techniques in dermatology. Artificial Intelligence (AI) has shown promising results in improving the diagnosis, treatment, and management of infectious skin disorders. This has the potential to significantly improve dermatological treatment by combining physician experience with data‐driven insights.

Objective: This review will look into the existing uses and future possibilities of AI technologies in infectious dermatology, including machine learning and deep learning. Its goal is to highlight major advances, identify gaps in understanding and technical advancement, and recommend viable future research directions.

Methods: A comprehensive literature search of the scientific literature was performed using well‐known databases such as PubMed, Google Scholar, and Embase. A specific set of phrases relevant to AI and infectious dermatology was used to ensure a thorough search. Articles were picked based on their relevance, timeliness, and quality, with a particular emphasis on research demonstrating how AI is being utilized to prevent, detect, diagnose, or manage infectious skin disorders.

Results: AI has made significant contributions to the management of infection in dermatology. It has improved diagnostic accuracy, predictive modeling of drug resistance, and individualized care regimens. Deep learning is used to evaluate clinical images, predictive models are developed to forecast antibiotic resistance, and AI‐powered diagnostic tools for uncommon infections. The assessment also throws light on AI's role in pandemic preparedness and response.