When less is more: Updates in active surveillance and watchful waiting in the management of prostate cancer.

BACKGROUND: Prostate cancer is the second most common cancer among men globally. A range of management options are available for prostate cancer, including surgery, radiation therapy, hormone therapy, chemotherapy, or surveillance. Conservative strategies include active surveillance and watchful waiting, which differ in their intent. OBJECTIVE: We provide a targeted instructive management algorithm for improving understanding of conservative strategies in prostate cancer. DISCUSSION: Active surveillance involves close monitoring with curative intent when there is evidence of disease progression. In contrast, watchful waiting is palliative in intent and focuses on delaying treatment until symptoms or complications develop. Conservative approaches have demonstrated similar long-term oncological outcomes to radical treatment, while reducing harm from overtreatment, and maintaining quality of life by avoiding potential side effects such as urinary incontinence and erectile dysfunction. The decision to employ a conservative approach is determined by both patient and disease factors. Conservative management strategies play a vital role in the management of prostate cancer.

A systematic review of large language models and their implications in medical education.

INTRODUCTION: In the past year, the use of large language models (LLMs) has generated significant interest and excitement because of their potential to revolutionise various fields, including medical education for aspiring physicians. Although medical students undergo a demanding educational process to become competent health care professionals, the emergence of LLMs presents a promising solution to challenges like information overload, time constraints and pressure on clinical educators. However, integrating LLMs into medical education raises critical concerns and challenges for educators, professionals and students. This systematic review aims to explore LLM applications in medical education, specifically their impact on medical students' learning experiences. METHODS: A systematic search was performed in PubMed, Web of Science and Embase for articles discussing the applications of LLMs in medical education using selected keywords related to LLMs and medical education, from the time of ChatGPT's debut until February 2024. Only articles available in full text or English were reviewed. The credibility of each study was critically appraised by two independent reviewers. RESULTS: The systematic review identified 166 studies, of which 40 were found by review to be relevant to the study. Among the 40 relevant studies, key themes included LLM capabilities, benefits such as personalised learning and challenges regarding content accuracy. Importantly, 42.5% of these studies specifically evaluated LLMs in a novel way, including ChatGPT, in contexts such as medical exams and clinical/biomedical information, highlighting their potential in replicating human-level performance in medical knowledge. The remaining studies broadly discussed the prospective role of LLMs in medical education, reflecting a keen interest in their future potential despite current constraints. CONCLUSIONS: The responsible implementation of LLMs in medical education offers a promising opportunity to enhance learning experiences. However, ensuring information accuracy, emphasising skill-building and maintaining ethical safeguards are crucial. Continuous critical evaluation and interdisciplinary collaboration are essential for the appropriate integration of LLMs in medical education.