Artificial Intelligence in Image Analysis For Dermatology And Dermatopathology: Advancements And Challenges

Emergence of Artificial Intelligence has evolved as a transformative tool for dermatology and dermatopathology, addressing significant hurdles such as high consultation costs, global dermatologist shortages, and diagnostic disparities. Therefore, this review delves into the progress that is being made in the areas related to AI applications with focus on its ability to diagnose high-resolution skin images towards better accuracy in diagnostics regarding melanoma, nevi, and inflammatory diseases. Using techniques like CNNs and deep learning, AI achieves sensitivity levels ranging from 58% to 96.1%, which is on par with human expertise. However, there are still many challenges, including dataset biases, underrepresentation of SOC, and reliance on outdated classification systems like the Fitzpatrick Skin Type (FST) scale. This paper will illustrate examples of solutions like Monk Skin Tone Scale, Fast Contrastive Unpaired Translation, (FastCUT) among others, to diminish this problem. Moreover, other ethical considerations like clarity and fairness and data confidentiality, among others, were observed. The review points toward multidisciplinary collaboration prospective clinical trials, and strict observance of regulatory compliance while dealing with AI models with great care for their trustworthiness and inclusivity. Thus, by overcoming these challenges, AI has the potential to revolutionize dermatological care with equity, accessibility, and effectiveness across diverse populations.