Given time and resource constraints in academic medical centers, creation of near peer mentoring opportunities has advantages for all individual and department stakeholders. However, many residents may feel unprepared and uncomfortable when asked to participate in these interactions. After reviewing available literature, advantages for incorporation of near peer mentoring and a suggested framework for incorporation of a mentoring curriculum into residency is provided.

To analyze trends from 2017 to 2024 in the number of group and state affiliations among Medicare-serving physicians, with emphasis on diagnostic radiologists.

By now, you’ve almost certainly heard of Chat Generative Pre-Trained Transformer (ChatGPT) and more recently, DeepSeek, artificial intelligence (AI) chatbots based on GPT large language models (LLMs) [1,2]. One way that a foundational GPT LLM can be adapted for targeted task-specific or subject matter domain-specific systems is by prompt engineering. Prompt engineering involves creating instructions for the model using natural language text questions or commands that may include relevant context, multiple steps, and reasoning, among other specifications.

Large language models (LLMs) are reshaping radiology through their advanced capabilities in tasks such as medical report generation and clinical decision support. However, their effectiveness is heavily influenced by prompt engineering—the design of input prompts that guide the model’s responses. This review aims to illustrate how different prompt engineering techniques, including zero-shot, one-shot, few-shot, chain of thought, and tree of thought, affect LLM performance in a radiology context.