Background: The integration of large language models (LLMs) into medicine has reshaped health care delivery, education, and research. Although proprietary models face challenges such as data privacy, regulation, and adaptability, DeepSeek, an open-source LLM, has emerged as a customizable and cost-effective alternative with significant potential for clinical and operational applications. However, the rapid expansion of research in this area necessitates a systematic mapping of its landscape, applications, and challenges. Objective: This study combines bibliometric analysis with a scoping review to systematically map and characterize the literature on DeepSeek's medical applications. The aims were to (1) analyze publication trends, leading contributors, and research themes and (2) identify primary application domains, strengths, limitations, and future directions. Methods: Following the framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a systematic search was conducted using PubMed, Web of Science, and Scopus from January 20, 2025, to November 30, 2025. Bibliometric analysis was then used to quantify publication trends, productivity, and research themes across 371 papers. The scoping review thematically synthesized the applications, strengths, and limitations of 353 original articles. Results: The publication output showed a progressive increase, with China (n=163), Turkey (n=52), and the United States (n=48) as leading contributors. Keyword co-occurrence analysis formed 7 clusters; the 3 most frequent keywords were "large language model," "artificial intelligence," and "patient education." DeepSeek has shown promising yet preliminary performance across multiple domains, including patient education, clinical decision support, medical education, workflow optimization, and medical research. The evidence base remains predominantly low in quality, with 66.6% (235/353) of original articles classified as low-quality evidence, consisting largely of unvalidated benchmarking, simulated cases, and single-center retrospective analyses. Only 6.8% (24/353) of studies met the criteria to be considered high quality, and prospective randomized trials assessing patient-relevant outcomes were notably absent. Conclusions: Publications on DeepSeek's medical applications increased progressively from January 2025 through November 2025, with China, Turkey, and the United States as the leading contributors. The scoping review found that DeepSeek has been evaluated across 5 domains (patient education, clinical decision support, medical education, workflow optimization, and research), with variable but often competitive performance relative to proprietary models. Strengths included readability, diagnostic accuracy in select specialties, cost-efficiency, and local deployability. Limitations included inconsistent cross-specialty performance, hallucinations, ethical concerns, data privacy issues, and regulatory gaps. The evidence base is predominantly low-quality and simulation-based, with few prospective trials or randomized controlled trials. These findings indicate that DeepSeek's clinical readiness varies, and future research should address prospective validation, multimodal capabilities, bias mitigation, human oversight, and equitable access.
Zhang et al. (Mon,) studied this question.