## 7 Overview of the Major Applications of AI in Dermatology
### Applications in Clinical Practice  
In dermatology, the most important aspect of AI is its use in supporting the diagnosis of skin cancer, ulcers, and psoriasis [197]. Due to the significant subjectivity and difficulty in quantitatively assessing skin lesions during traditional examinations, the use of AI can significantly support the accuracy of such examinations [198].  
Aysa [199] is a symptom-checking mobile application created by VisualDx (NY, US). It integrates a clinically focused search engine with a carefully curated database of over 120,000 medical images covering around 200 dermatological conditions. Additionally, the app adapts its suggestions based on the user's health history, offering a more tailored experience. In [200], the authors conducted clinical studies on the efficacy of Aysa in a semi-urban town in India. The authors tested the performance of the app using measures such as the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, F1-score, and \(\chi^{2}\) test. The analysed decision model, the core of the Aysa application,showed promising results. Notably, the sensitivity of the most probable decision (top-1) was 71% (95% CI: 61.5-74.3%).  
[PERSON] et al. [2011] developed the PanDerm neural network model, which was trained on over two million images across four imaging modalities. The authors showed that PanDerm outperformed clinicians by 10.2% in early-stage melanoma detection. The authors also demonstrated that the model supported physicians' work by improving their overall diagnostic accuracy, based on dermoscopic images, by 11%. Notably, this model also demonstrated strong results across various demographic groups.  
[PERSON] et al. [202] developed a HOT (Hierarchical-Out-of-Distribution Clinical Triage) neural network model, trained on a dataset of approximately 208,000 lesion images, that generates three levels of diagnostic results: the classification of skin lesions, warnings for unknown cases, and recommendations for dermatoscopy. The authors showed that the sensitivity of the most general level of diagnosis (benign/malignant) was 88.14% (95% CI: 87.42-88.51%). At the most detailed level of analysis, encompassing 44 skin lesion classes, the model achieved a sensitivity score of 63.90% (95% CI: 62.27-65.61%). It is worth noting that this model has not yet received official clinical test results. However, this method of classifying skin lesions is innovative, i.e., utilising different levels of detail, which may significantly contribute to promising human-AI cooperation in clinical settings.  
An interesting aspect in clinical applications of AI in dermatology is the accuracy of diagnoses made by doctors and intelligent systems. In [203], the authors compared an intelligent image classification system for skin lesions with an expert dermatologist and a non-expert. The AI-based system demonstrated sensitivity of 93.59% and specificity of 70.42% in identifying malignant lesions. The AI-based system showed comparable sensitivity and significantly greater specificity than both the expert dermatologist and the non-expert.