Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check

• Published in: Journal of investigative dermatology Vol. 144; no. 3; pp. 492 - 499
• Main Authors: Brancaccio, Gabriella, Balato, Anna, Malvehy, Josep, Puig, Susana, Argenziano, Giuseppe, Kittler, Harald
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2024
• Subjects: Algorithms; Artificial Intelligence; Convoluted neural network; Dermoscopy; Humans; Image Interpretation, Computer-Assisted; Melanoma; Mobile apps; Primary care; Skin; Skin Neoplasms - diagnosis; OCT; CNN; BCC; TBP; CI; Melanoma; AI; DEJ; Dermoscopy; GP; Mobile apps; Primary care; 3D; SCC; LC-OCT; Convoluted neural network; RCM
• ISSN: 0022-202X; 1523-1747; 1523-1747
• DOI: 10.1016/j.jid.2023.10.004

The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.