This study presents a systematic evaluation of DeepSeek, a cutting-edge Chinese-developed large language model (LLM), focusing on its application in editing and refining Chinese-language abstracts for medical journals. It employed a comprehensive methodology, analyzing 60 medical research abstracts from the Journal of Jilin University (Medical Edition). These texts were processed using DeepSeek-R1, the latest version of the model released in January 2025, which was specifically integrated into Microsoft Word to simulate practical editorial workflows. To ensure methodological rigor, this study implemented a dual evaluation framework combining quantitative metrics with qualitative expert assessments conducted by three senior medical editors with extensive publishing experience. In terms of basic language refinement, the model demonstrated remarkable proficiency, accurately identifying and correcting grammatical errors in 95% of cases compared to benchmarks set by human editors. It performed particularly well in handling complex Chinese sentence structures, including resolving issues with subject-verb agreement, proper comma usage, and logical coherence. Moreover, the model exhibited strong performance in language simplification, reducing wordiness by 44.7% across the test corpus while preserving essential meaning, a capability that could substantially improve the clarity and accessibility of medical research reporting. However, the evaluation also revealed critical limitations that currently restrict DeepSeek's utility for comprehensive medical editing. Notably, the model struggled with domain-specific challenges, achieving only 22.4% accuracy in standardizing domain-specific medical terminology. Several technical factors underlying these limitations were identified. First, the model's training data appears insufficient to comprehensively cover the full spectrum of medical sub-specialties and their evolving terminologies. Second, DeepSeek exhibits constrained ability to contextualize information within the rigorous framework of medical research reporting, occasionally prioritizing linguistic fluency over scientific precision. Third, the analysis suggests that the model has difficulty keeping pace with rapid advancements in medical science, occasionally providing outdated terminology or conceptual frameworks. Despite these limitations, the model's processing speed, approximately 20 times faster than human editors, positions it as a transformative tool for revolutionizing manuscript turnaround times, particularly during periods of high submission volume. Its consistent performance in fundamental language polishing suggests valuable applications for initial manuscript screening and formatting standardization. A tiered editorial workflow is recommended, where DeepSeek handles initial language refinement and formatting, followed by specialized human editors focusing on content validation and scientific accuracy. To maximize future potential of the model, three key development directions are proposed: (1) expanding medical domain-specific training data, particularly for emerging specialties and technologies; (2) developing hybrid models that integrate linguistic analysis with medical knowledge graphs; and (3) creating customizable modules that journals can adapt to their specific stylistic and terminological requirements. These findings.