The field of Large Language Models (LLMs) in medical applications is moving towards addressing the critical issue of outdated knowledge and memorization. Researchers are investigating the prevalence and characteristics of memorization in LLMs, as well as its implications for medical applications. The reliance on static training data and the potential for harmful advice or clinical reasoning failures are major concerns. To mitigate these risks, studies are exploring the influence of obsolete pre-training data and training strategies, and proposing novel question-answering datasets and evaluation frameworks. Additionally, the phenomenon of knowledge collapse in LLMs is being examined, where factual accuracy deteriorates while surface fluency persists. Noteworthy papers include: Facts Fade Fast, which introduces novel QA datasets to evaluate LLMs' reliance on outdated knowledge. Knowledge Collapse in LLMs defines a distinct three-stage phenomenon and proposes domain-specific synthetic training as a mitigation strategy. Memorization in Large Language Models in Medicine presents a comprehensive evaluation of memorization in LLMs and offers practical recommendations to facilitate beneficial memorization and minimize harmful memorization.