Large Language and Vision Assistant in Dermatology: A Game Changer or Just Hype?

Clin Exp Dermatol. 2024 Apr 4:llae119. doi: 10.1093/ced/llae119. Online ahead of print.

Abstract

The integration of artificial intelligence (AI) in healthcare, particularly in the field of dermatology, has experienced significant progress through the creation of advanced tools such as the Large Language and Vision Assistant (LLaVA). This comprehensive review examines whether LLaVA represents a significant breakthrough or merely a passing trend in dermatological practice. By incorporating both language and visual analysis capabilities, LLaVA aims to support enhanced diagnostic accuracy, patient engagement, and customized treatment planning, as evidenced by current research and case studies. However, its practical utility in a clinical setting remains a subject of debate. We explore the visual assistant chatbot's potential in improving diagnostic precision, especially in analyzing skin lesions and conditions that are visually complex. The tool's capacity to process and interpret dermatological images using advanced algorithms could aid clinicians in early detection and management of skin diseases. Furthermore, LLaVA's interactive nature potentially improves patient education and adherence to treatment protocols. Despite these advantages, there are noteworthy limitations and risks. The accuracy of LLaVA in handling atypical or rare dermatological cases is an area of concern. The tool's reliance on existing medical data raises questions about bias and the generalizability of its findings. Additionally, ethical considerations, such as patient data privacy and the potential for overreliance on AI in clinical decision-making, are critical issues that need addressing. This article aims to provide dermatologists with a comprehensive understanding of large language and visual assistant's capabilities and limitations. We discuss practical guidelines for its integration into research and clinical educational augmentation, ensuring that dermatologists can make informed decisions about employing this technology for the enhancement of patient care and treatment outcomes. The question remains: is LLaVA a game changer in dermatology, or is it just hype? This review endeavours to answer this, establishing a foundation for knowledgeable and efficient application of visual AI chatbots in dermatology practices.