Changes in nail color can provide important clues of underlying systemic and skin disease. In particular, white discoloration (leukonychia) has a high prevalence with a wide array of potential relevant causes, from simple manicure habits to life-threatening liver or kidney failure. Therefore, a reliable assessment of the patient with leukonychia is essential. In the past, two classifications for leukonychia have been presented. The morphological classifies the nail according to the distribution of the white lines: total, partial, transversal, and longitudinal leukonychia. Mees’ and Muehrcke’s lines are examples of transversal leukonychia, while Terry’s and Lindsay’s nails are examples of total and partial leukonychia. The anatomical classifies according to the structure responsible for the white color: the nail plate in true leukonychia, the nail bed in apparent leukonychia, and the surface only in pseudoleukonychia. In this review, both morphological and anatomical features have been combined in an algorithm that enables clinicians to approach leukonychia efficiently and effectively.

Introduction: Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. Results: Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. Discussion: AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusions: AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems.