The Role of ChatGPT in Dermatology Diagnostics

Abstract

Artificial intelligence (AI), especially large language models (LLMs) like ChatGPT, has disrupted different medical disciplines, including dermatology. This review explores the application of ChatGPT in dermatological diagnosis, emphasizing its role in natural language processing (NLP) for clinical data interpretation, differential diagnosis assistance, and patient communication enhancement. ChatGPT can enhance a diagnostic workflow when paired with image analysis tools, such as convolutional neural networks (CNNs), by merging text and image data. While it boasts great capabilities, it still faces some issues, such as its inability to perform any direct image analyses and the risk of inaccurate suggestions. Ethical considerations, including patient data privacy and the responsibilities of the clinician, are discussed. Future perspectives include an integrated multimodal model and AI-assisted framework for diagnosis, which shall improve dermatology practice.

Keywords: ChatGPT; dermatology; diagnosing.

Figure 1

Illustration of a multimodal large language model (LLM) system integrating both image and text data. The image pathway is encoded via convolutional neural networks (CNNs) or vision transformers (ViTs), while text inputs (e.g., patient history) are tokenized and processed by a large language model (e.g., GPT-4). A projection module aligns visual embeddings with the text domain, enabling the model to generate comprehensive outputs such as diagnostic suggestions, patient education, and clinical explanations.