在不断发展的医学研究和医疗保健领域，大量的科学文章既是知识的宝库，也是艰巨的挑战。研究人员、临床医生和数据科学家努力处理大量非结构化信息，寻求提取有意义的见解，以推动生物医学领域的进步，包括研究趋势、患者护理、药物发现和疾病理解。本文利用乳腺癌研究的主题提取算法来阐明该领域的当前趋势和未来的发展方向。我们利用 TextRank 和大型语言模型 (LLM)，使用 TripleA 工具提取该领域的主题，分析和比较结果。

In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of unstructured information, seeking to extract meaningful insights that can drive advancements in the biomedical domain including, research trends, patient care, drug discovery, and disease understanding. This paper utilizes the topic extraction algorithms on Breast Cancer Research to shed light on the current trends and the path to follow in this field. We utilized TextRank and Large Language Models (LLM) using the TripleA tool to extract topics in the field, analyzing and comparing the results.