Systematic literature reviews (SLRs) are foundational for research but resource-intensive to conduct. With the rise of large language models (LLMs) such as ChatGPT, generative AI (GenAI) tools are being increasingly explored for their potential to support and transform the SLR process. This study presents a systematic review of peerreviewed articles that examine how LLM-based GenAI tools are used in different SLR phases. Following the PRISMA 2020 guidelines, we screened 1,846 publications published since January 2021 until April 2025 and selected 54 for in-depth analysis. Each study was coded by review phase, prompting approach, automation level, validation type and challenges. Our findings show that GenAI is most often used to support in the screening, search, and writing phases, typically through Basic Prompting and under human oversight. While many studies report efficiency gains, concerns remain regarding validity, transparency, and methodological rigor. Moreover, GenAI is frequently applied to isolated tasks but is rarely embedded in a structured, methodologically guided review processhighlighting the need for clearer phase-specific guidance and standards. We offer a structured, phase-specific synthesis that highlights both the promise and the current limitations of GenAI in literature reviews and thereby offer practical recommendations for the responsible use of GenAI in literature reviews.