## 1 Introduction
One of the most pressing needs in the circular economy is the effective sorting of textiles for recycling and reuse. Currently, much of this sorting is accomplished manually, especially in countries like Australia, where the charitable reuse sector relies heavily on volunteer labor. Existing technological systems for garment and textile sorting, such as near-infrared spectroscopy, have primarily been explored for use within commercial textile sorting organizations to support fiber separation for textile recycling [10]. As a result, it is unclear the extent to which these technologies are viable for the charitable or not-for-profit reuse sector, which is characterized by a subjective, often volunteer workforce who typically conduct localized value-based assessments around brand value, garment archetype, or current trend alignment. AI-driven systems, such as those employing convolutional neural networks (CNNs) and hybrid models, can classify textiles by type, physical condition, and recyclability, addressing a critical bottleneck in textile waste management. Beyond recycling, AI has the potential to play a transformative role in second-hand clothing markets. Automated systems can be used to assess garment quality, detect defects, and estimate wearability, enabling accurate pricing and categorization. This not only enhances consumer trust but also supports charitable organizations in distributing usable garments effectively in the future. This systematic literature review (SLR) aims to provide a comprehensive understanding of the current landscape of existing AI technology that is relevant to sorting textiles and garments and explores how these technologies can be implemented to improve sorting practices. While previous systematic literature reviews have examined the potential for use of AI technologies in the fashion industry [1; 2], these reviews included a wide variety of AI technologies and applications, including the design and sale of goods. Study [1] reviewed the role of artificial intelligence (AI) in enhancing sustainability within the fashion industry from 2010 to 2022. It highlights AI's diverse applications, including supply chain management, creative design, and waste control, emphasizing its potential to improve environmental sustainability. This study acknowledges the benefits of AI, such as efficient resource management and customer satisfaction, while also noting challenges like data requirements and implementation costs. Overall, AI is seen as a crucial tool for guiding the fashion industry towards a more sustainable future, despite some existing limitations. Study [2] systematically reviewed circular economy strategies in the textile industry. It identifies key strategies such as reuse, recycling, repair, and reduction, evaluating them through environmental, social, and economic lenses. The study highlights the potential of reuse to reduce waste and social inequality, while recycling faces technological and policy barriers. Repair extends garment lifespans but is limited by service accessibility and consumer knowledge. Reduction focuses on sustainable materials, challenged by the fast fashion model. Overall, the review underscores the need for substantial investments and supportive policies to advance sustainability in the textile industry. However, a systematic review examining existing AI and machine vision technologies that could support the fashion industry to transition to a circular economy, with a focus on existing AI technologies for the purpose of sorting textile waste and discarded garments, does not appear to have been undertaken. By examining the recent academic literature, this unique review aims to remedy this gap by highlighting the potential of AI to support the secondhand textile industry, promoting CE strategies through increasing reuse of existing textiles and garments.