Effective management of atopic dermatitis (AD) has been one of the top priorities for physicians over the years, as quality of life in these patients is compromised due to the disruptive symptoms that they experience, which include itchiness and skin inflammation. As diagnosis of AD is primarily clinical, it may be subjective, resulting in variability in its diagnosis. In the era of artificial intelligence (AI), various trends have been observed that utilize AI in multiple sectors, having a major impact, especially in healthcare. The application of AI in diagnosing and identifying AD cases could lead to more accurate, early, and standardized identification of the condition, thus optimizing patient outcomes, says GlobalData, a leading data and analytics company.
Filippos Maniatis, Healthcare Analyst at GlobalData, comments: “AI offers innovative solutions to challenges in the field of healthcare through the simulation of human intelligence in machines, with advancements paving the way for significant improvements in diagnostics, drug discovery, and personalized medicine. The absence of objective biomarkers for definitive diagnosis and assessment of disease severity adds complexity to the diagnostic landscape. AI-powered image recognition can determine characteristic patterns of AD in skin lesion images, and through machine training, the diagnostic accuracy for AD can be significantly improved over time.”
More personalized treatment plans, continuous monitoring, and leveraging of technological advancements can be achieved through AI. For instance, wearable devices equipped with AI technology could provide ongoing monitoring of skin conditions, delivering real-time data vital for disease management.
As noted by Lee et al., who investigated the use of an accelerometer-equipped wristwatch in AD patients to identify scratching tendencies, it was shown that when compared to infrared video surveillance, the wristwatch exhibited remarkable accuracy, with detection rates ranging from 98.5–99.0% for right-hand scratching motions and 93.3–97.6% for left-hand scratching. Another study by Maulana et al. also investigated the severity categorization of AD through AI models, focusing on underrepresented populations, and successfully validated their model in showing significant promise in aiding dermatologists and general practitioners to classify AD severity levels more accurately.
Maniatis concludes: “Continuous monitoring may assist physicians in making effective adjustments to treatment plans upon disease severity changes (e.g. flare-ups) and has already attracted the interest of multiple technology companies. These technologies highlight their ability to improve current diagnosis and management of the disease. Collaborative efforts between AI specialists, clinicians, and researchers are vital to fully understand AI’s potential in improving the management of AD.”
