DeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier

Abstract

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.

Keywords: ai hallucinations and bias in medicine; deepseek deepthink r1 open-source ai; ethical considerations in ai-driven healthcare; generative artificial intelligence (gai) in healthcare; healthcare data privacy and ai compliance; hipaa and gdpr ai compliance; large language models (llms) in medicine; medical education and ai integration; offline ai deployment for healthcare; open-source ai for medical research.