In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

Computer vision (CV) is a highly-relevant field within the artificial intelligence that enables machines to interpret and analyse visual data from the physical world, mimicking human vision. In the mining industry, CV applications have become popular as a means to address critical challenges such as optimizing resource extraction, improving safety, and minimizing environmental impact. However, existing research often addresses specific use cases, resulting in a fragmented understanding of CV’s broader applications across the sector. Based on the PRISMA 2020 guidelines, this systematic literature review (SLR) addresses this gap by consolidating diverse research into a broad resource. Peer-reviewed journal articles (2019–2024) from Web of Science and Scopus were reviewed, with 101 studies meeting inclusion criteria. The findings indicate that CV technologies have evolved from isolated tools to interconnected analytical ecosystems spanning the entire mining value chain. Applications range from safety monitoring and ore detection to environmental assessments, enhanced by multi-sensor fusion and deep learning. However, a critical validation gap emerges: while safety rhetoric dominates adoption justifications, fewer than 25% of studies provide quantitative validation. Environmental robustness remains the primary technical barrier, with methodological fragmentation undermining deployment confidence. The field requires a shift toward comprehensive field validation, standardized frameworks, and responsible deployment. This transformation signals the emergence of predictive, environmentally conscious mining practices that will fundamentally reshape resource extraction while advancing CV capabilities for sustainable industrial applications.