Large Language Models for Transforming Healthcare: A Perspective on DeepSeek‐R1 - Jinsong Zhou; Yuhan Cheng; Sixu He; Yingcong Chen; Hao Chen (MedComm – Future Medicine)
ABSTRACT DeepSeek‐R1 is an open‐source Large Language Model (LLM) with advanced reasoning capabilities. It has gained significant attention for its impressive advantages including low costs and visualized reasoning steps. Recent advancements in reasoning LLMs like ChatGPT‐o1 have significantly exhibited their considerable reasoning potential, but the closed‐source nature of existing models limits customization and transparency, presenting substantial barriers to their integration into healthcare systems. This gap motivates the exploration of DeepSeek‐R1 in the medical field. Thus, we comprehensively review the transformative potential, applications, and challenges of DeepSeek‐R1 in healthcare. Specifically, we investigate how DeepSeek‐R1 can enhance clinical decision support, patient engagement, and medical education to help for clinic, outpatient and medical research. Furthermore, we critically evaluate challenges including modality limitations (text‐only), hallucination risks, and ethical issues, particularly related to patient autonomy and safety‐focused recommendations. By assessing DeepSeek‐R1′s integration potential, this perspective highlights promising opportunities for advancing medical AI while emphasizing necessary improvements to maximize clinical reliability and ethical compliance. This paper provides valuable guidance for future research directions and elucidates practical application scenarios for DeepSeek‐R1′s successful integration into healthcare settings.