The increasing demand for multilingual capabilities in healthcare underscores the need for AI models adept at processing diverse languages, particularly in clinical documentation and decision-making. Arabic, with its complex morphology, syntax, and diglossia, poses unique challenges for natural language processing (NLP) in medical contexts. This case study evaluates Sporo AraSum, a language model tailored for Arabic clinical documentation, against JAIS, the leading Arabic NLP model. Using synthetic datasets and modified PDQI-9 metrics modified ourselves for the purposes of assessing model performances in a different language. The study assessed the models' performance in summarizing patient-physician interactions, focusing on accuracy, comprehensiveness, clinical utility, and linguistic-cultural competence. Results indicate that Sporo AraSum significantly outperforms JAIS in AI-centric quantitative metrics and all qualitative attributes measured in our modified version of the PDQI-9. AraSum's architecture enables precise and culturally sensitive documentation, addressing the linguistic nuances of Arabic while mitigating risks of AI hallucinations. These findings suggest that Sporo AraSum is better suited to meet the demands of Arabic-speaking healthcare environments, offering a transformative solution for multilingual clinical workflows. Future research should incorporate real-world data to further validate these findings and explore broader integration into healthcare systems.

本研究针对阿拉伯语医疗领域的语言处理挑战，提出了一种新的语言模型Sporo AraSum，旨在优化临床文档的生成。研究表明，Sporo AraSum在量化性能和定性属性方面显著优于现有的JAIS模型，能够更准确地处理阿拉伯语的语言细微差别，减少AI幻觉的风险，从而提高阿拉伯语医疗环境中的临床工作流。未来研究将通过真实数据进一步验证这些发现。

在阿拉伯语中提升复杂医疗交流的Sporo AraSum：超越现有大型语言模型