Large language models (LLMs) have been widely recognized for their ability to encode clinical knowledge and effectively summarize medical texts. However, despite these capabilities, there is still a lack of comprehensive understanding regarding the optimal strategies for prompting or fine-tuning these models for specific tasks, particularly when dealing with non-English corpora. To address this gap, we conducted a systematic investigation into various in-context learning (ICL) strategies. Our study focused on evaluating a broad and diverse set of state-of-the-art open-source large language models (OS-LLMs) with the objective of determining their effectiveness in summarizing key findings from stroke CT reports.