# Revolutionizing dermatopathology using AI in skin diagnostics: scoping review

## TL;DR

This review explores how AI models are transforming skin disease diagnosis, highlighting their strengths and limitations in dermatopathology.

## Contribution

The study provides a scoping review of AI applications in skin diagnostics, emphasizing the shift from traditional models to LLM-based approaches.

## Key findings

- CNN- and ViT-based models are widely used, but LLM-based models like SkinGPT are emerging for interactive analysis.
- AI models perform well for common skin diseases but struggle with rare or underrepresented conditions.
- Most AI models require better clinical validation and regulatory standards before widespread clinical use.

## Abstract

AI models are becoming is increasingly used to enhance skin disease diagnosis and treatment. This scoping review complies with the PRISMA-ScR guidelines and after considering the inclusion and exclusion criteria, 12 articles published between 2017 and 2024 were considered. Majority of the publications are published from US and China. Among the selected studies, CNN- and ViT-based AI models were the most commonly used in literature, while LLM-based models (such as SkinGPT and Gemini-based models) appear in recently times more frequently to conduct interactive analysis for users. Recent studies have increasingly featured LLM-based models (e.g., SkinGPT, Gemini), indicating their growth as novel architectures in contrast to traditional CNN and ViT approaches. Among the diseases, the studied mainly covered melanoma, nevi, basal cell carcinoma, keratinocyte carcinoma, seborrheic keratosis, colorectal adenoma, etc. Our research reveals that while AI models excel in diagnosing prevalent and well-documented skin problems, their diagnostic efficacy significantly diminishes for rare or underrepresented diseases, highlighting the necessity for more robust, diversified, and clinically validated models. AI models are often too generic for multiple skin diseases. The studies utilized both private clinical data and public accessible resources, including ISIC and MoleMap. Majority of the AI models need improved clinical validation and regulatory standards covering ethical and legal standards to be considered as a tool for healthcare service providers. Despite these constraints, the reviewed studies indicate that AI models can enhance dermatopathology by increasing lesion classification precision, facilitating early detection, and reducing diagnostic strain; underscoring their prospective significance for clinicians and patients.