Temsah Abdulrahman, Alhasan Khalid, Altamimi Ibraheem, Jamal Amr, Al-Eyadhy Ayman, Malki Khalid H, Temsah Mohamad-Hani
Software Engineering, Alfaisal University, Riyadh, SAU.
Pediatric Department, College of Medicine, King Saud University, Riyadh, SAU.
Cureus. 2025 Feb 18;17(2):e79221. doi: 10.7759/cureus.79221. eCollection 2025 Feb.
Generative Artificial Intelligence (GAI) has driven several advancements in healthcare, with large language models (LLMs) such as OpenAI's ChatGPT, Google's Gemini, and Microsoft's Copilot demonstrating potential in clinical decision support, medical education, and research acceleration. However, their closed-source architecture, high computational costs, and limited adaptability to specialized medical contexts remained key barriers to universal adoption. Now, with the rise of DeepSeek's DeepThink (R1), an open-source LLM, gaining prominence since mid-January 2025, new opportunities and challenges emerge for healthcare integration and AI-driven research. Unlike proprietary models, DeepSeek fosters continuous learning by leveraging publicly available open-source datasets, possibly enhancing adaptability to the ever-evolving medical knowledge and scientific reasoning. Its transparent, community-driven approach may enable greater customization, regional specialization, and collaboration among data researchers and clinicians. Additionally, DeepSeek supports offline deployment, addressing some data privacy concerns. Despite these promising advantages, DeepSeek presents ethical and regulatory challenges. Users' data privacy worries have emerged, with concerns about user data retention policies and potential developer access to user-generated content without opt-out options. Additionally, when used in healthcare applications, its compliance with China's data-sharing regulations highlights the urgent need for clear international data privacy and governance. Furthermore, like other LLMs, DeepSeek may face limitations related to inherent biases, hallucinations, and output reliability, which warrants rigorous validation and human oversight before clinical application. This editorial explores DeepSeek's potential role in clinical workflows, medical education, and research while also highlighting its challenges related to security, accuracy, and responsible AI governance. With careful implementation, ethical considerations, and international collaboration, DeepSeek and similar LLMs could enhance healthcare innovation, providing cost-effective, scalable AI solutions while ensuring human expertise remains at the forefront of patient care.
生成式人工智能（GAI）推动了医疗保健领域的多项进步，诸如OpenAI的ChatGPT、谷歌的Gemini和微软的Copilot等大型语言模型（LLM）在临床决策支持、医学教育和研究加速方面展现出了潜力。然而，它们的闭源架构、高昂的计算成本以及对专业医疗环境的有限适应性仍然是广泛应用的关键障碍。如今，随着自2025年1月中旬以来崭露头角的开源大型语言模型DeepSeek的DeepThink（R1）的兴起，医疗保健整合和人工智能驱动的研究出现了新的机遇和挑战。与专有模型不同，DeepSeek通过利用公开可用的开源数据集促进持续学习，这可能增强其对不断发展的医学知识和科学推理的适应性。其透明的、社区驱动的方法可能实现更大程度的定制、区域专业化以及数据研究人员和临床医生之间的协作。此外，DeepSeek支持离线部署，解决了一些数据隐私问题。尽管有这些有前景的优势，但DeepSeek也带来了伦理和监管方面的挑战。用户对数据隐私的担忧已经出现，涉及对用户数据保留政策的担忧以及开发者在没有退出选项的情况下可能访问用户生成内容的问题。此外，在医疗保健应用中使用时，它对中国数据共享法规的遵守凸显了明确国际数据隐私和治理的迫切需求。此外，与其他大型语言模型一样，DeepSeek可能面临与固有偏差、幻觉和输出可靠性相关的局限性，这在临床应用前需要进行严格验证和人工监督。这篇社论探讨了DeepSeek在临床工作流程、医学教育和研究中的潜在作用，同时也强调了其在安全性、准确性和负责任的人工智能治理方面的挑战。通过谨慎实施、伦理考量和国际合作，DeepSeek及类似的大型语言模型可以促进医疗保健创新，提供具有成本效益、可扩展的人工智能解决方案，同时确保人类专业知识始终处于患者护理的前沿。
