Modern medicine is undergoing a technological revolution, and dermatology occupies a leading position here. The introduction of digital innovations helps cope with the shortage of specialized professionals and the growing patient flow [1]. Artificial intelligence (AI) is becoming a powerful tool that significantly improves the quality of medical care.
Main Areas of AI Application in Dermatology
Smart algorithms are actively researched and applied in three main areas: diagnosis of malignant skin neoplasms, detection of non-oncological pathologies, and analysis of additional imaging data [1]. The initial developments focused precisely on skin tumors (such as melanoma) because such images are easy to standardize [2]. Today, the capabilities of algorithms have gone far beyond oncology.
Diagnostic Accuracy: From Melanoma to Psoriasis
Modern neural networks demonstrate impressive results. In terms of sensitivity and specificity in recognizing melanoma, basal cell and squamous cell skin cancer, they are not inferior to experienced doctors [1]. Moreover, similar successes have been recorded in the analysis of clinical and dermoscopic images of inflammatory and autoimmune pathologies [1, 2]. Most often, studies examine such chronic dermatoses as:
- Psoriasis (including calculation of the PASI index) [2, 6]
- Atopic dermatitis and eczema [1, 6]
- Acne [1, 6]
- Hidradenitis [2]
Integration with Imaging Methods and Related Fields
Machine learning algorithms are effective not only when evaluating standard photographs. They are successfully combined with advanced imaging methods such as dermoscopy, optical coherence tomography, and reflectance confocal microscopy [1]. This allows for a deeper analysis of tissue structure. In addition, psychodermatology is actively developing, where AI helps find biomarkers linking skin manifestations with anxiety disorders and dysmorphophobia [3].
Advantages and Prospects of Technologies
The introduction of digital systems allows for the automation of routine tasks, reducing the burden on clinicians and ensuring a personalized approach to treatment [3]. However, for widespread implementation in clinical practice, large-scale multicenter studies on diverse patient cohorts are required to confirm the reliability of algorithms in real-world conditions [4].
Sources
- 1. AI-Based Diagnostic Tools in Dermatology: A Review
- 2. Artificial Intelligence in Inflammatory Dermatoses: Current Status and Prospects
- 3. Artificial Intelligence in Psychodermatology: Applications and Impact on Clinical Practice
- 4. Artificial Intelligence in Inflammatory Bowel Disease
- 5. Artificial Intelligence in Inflammatory Bowel Disease (Saudi Journal of Gastroenterology)
- 6. New AI Applications in Chronic Dermatological Diseases
- 7. Artificial Intelligence and Machine Learning in Respiratory Medicine
- 8. AI Methods for Differential Diagnosis of Irritable Bowel Syndrome and IBD
Disclaimer: The information provided in the article is for reference purposes only and does not replace an in-person consultation with a dermatologist.