The field of large language models (LLMs) is moving towards addressing the critical issue of bias in these models. Recent research has highlighted the prevalence of bias in LLMs, which can lead to misdiagnosis, inappropriate treatment, and exacerbation of health inequities. To combat this, researchers are developing innovative methods to detect and mitigate bias in LLMs. These methods include creating high-quality benchmarking datasets, proposing cognitive debiasing approaches, and investigating stereotype-aware unfairness in LLM-based recommendations. Noteworthy papers in this area include 'Bias in Large Language Models Across Clinical Applications: A Systematic Review', which reveals pervasive bias across various LLMs and clinical applications, and 'Cognitive Debiasing Large Language Models for Decision-Making', which proposes a self-debiasing method to enhance the reliability of LLMs. Additionally, 'Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings' and 'Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations' are also making significant contributions to this field.