With the prevalence of online news services, personalized news recommendation (PNR) has played an indispensable role in meeting users' needs and mitigating information overload, with the aim of providing news articles that cater to user preferences. Despite significant progress made in the field of PNR over the past few decades, their performances are still hindered by some limitations, such as insufficient news modeling, difficulties in effectively modeling diverse user interests, and ignorance of fine-grained matching signals. It is fortunate that the emergence of large language models (LLMs) provides a promising insight into empowering the capabilities of news recommendation. Known for their impressive capabilities of natural language understanding and generation, LLMs have achieved disruptive achievements in various natural language processing (NLP) tasks, which motivates us to integrate LLMs into news recommendation and benefits from them to make up existing deficiencies. In this paper, we conduct a comprehensive review of current efforts made towards utilizing LLMs for PNR, with a focus on three core modules involved in the news recommendation process, i.e., news modeling, user modeling, and accurate matching. We systematically discuss and analyze relevant works under each focus. In addition, we point out several potential research directions to provide more inspiration for future investigation in this thriving field.