The field of clinical text analysis and generation is rapidly advancing, with a focus on developing innovative solutions to improve patient care and outcomes. Recent research has centered on leveraging large language models (LLMs) to extract critical patient information from electronic health records, classify radiological reports, and generate factual clinical summaries. These approaches have shown promise in enhancing the accuracy and usability of clinical text analysis, enabling clinicians to make more informed decisions and improving patient care outcomes. Noteworthy papers in this area include: Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning, which introduces a novel approach to extracting critical patient information from EHRs. MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification, which presents a multilingual and computationally efficient approach for radiological report classification. MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries, which introduces a framework for scalable evaluation of factual accuracy in LLM-generated clinical text.