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 the diagnosis of AD is primarily clinical, it may be subjective, resulting in variability in its diagnosis. The absence of objective biomarkers for definitive diagnosis and assessment of disease severity adds complexity to the diagnostic landscape.
In the era of AI, various trends have been observed that utilise AI in multiple sectors, having a major impact, especially in healthcare. 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 personalised medicine. The application of AI in diagnosing and identifying AD cases could lead to more accurate, early, and standardised identification of the condition, thus optimising patient outcomes. 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 (Jain et al, 2024). AI’s ability to process patient data sets may enable the identification of risk factors and the potential prediction of AD, which may assist in better disease monitoring and effective condition management. Through the incorporation of AI into the diagnostic paradigm, more personalised treatment plans, continuous monitoring, and leveraging of technological advancements can be achieved. Such technologies have been previously investigated, including studies focusing on wearable devices. Specifically, 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 (Lee et al, 2015 and Ikoma et al, 2019). Such continuous monitoring may assist physicians in making effective adjustments to treatment plans upon disease severity changes (eg flare-ups) and has already attracted the interest of Nestlé Skin Health and Apple Inc., with the two entities collaborating to provide this invention to patients through a smartwatch application, offering an objective and indirect measure of pruritus severity in patients with AD. Another study by Maulana et al has also investigated the severity categorisation of AD through AI models. Specifically, Maulana et al focused 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, resulting in better diagnoses and improved patient care, providing a more inclusive skin disease diagnosis (Maulana et al, 2023).
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Such technologies that were previously described further showcase the multiple implications of AI in AD, also highlighting its ability to improve current diagnosis and management of the disease. Collaborative efforts between AI specialists, clinicians, and researchers are vital to fully understanding AI’s potential in improving the management of AD.
