Chinese AI startup DeepSeek is recruiting interns to label medical data for its AI-driven healthcare tools.

The positions are advertised on Boss Zhipin and offer 500 yuan (US$70) per day for candidates in Beijing who can work four days a week.

Applicants must have a medical background and be either fourth-year undergraduates or master’s degree holders.

Experience with large language models (LLMs), Python programming, and prompt writing for AI models is also required.

This is the first time DeepSeek has publicly referred to “medical data” in its labeling initiatives.

DeepSeek’s AI models are used in at least 300 hospitals across China for clinical diagnostics and medical decision support.

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🔗 Source: South China Morning Post

🧠 Food for thought

1️⃣ Medical domain expertise is critical for healthcare AI training

DeepSeek’s specific recruitment of interns with medical backgrounds for data labeling highlights a critical factor in healthcare AI development: domain expertise.

Research shows that AI algorithms can only perform accurately in medical applications when trained on properly labeled, high-quality healthcare data that accounts for clinical nuances 1.

This specialized approach addresses one of the key implementation challenges in healthcare AI, data quality and relevance, which can significantly impact diagnostic accuracy and patient outcomes 2.

The requirement for candidates to have both medical knowledge and technical skills (Python coding, LLM experience) demonstrates the interdisciplinary expertise needed to develop effective healthcare AI systems.

By prioritizing medical domain knowledge in its data labeling process, DeepSeek is taking a human-centered approach that research has shown is essential for building AI systems that genuinely complement healthcare professionals rather than attempting to replace them 2.

2️⃣ Open-source AI models are accelerating healthcare adoption in China

The rapid adoption of DeepSeek’s open-source models by 300 Chinese hospitals represents a significant real-world implementation of AI in clinical settings.

Open-source AI models provide organizations with greater control over sensitive patient data, a crucial factor for regulatory compliance in healthcare environments 3.

Studies show that open-source approaches in healthcare AI foster collaboration among researchers and developers, potentially accelerating innovation in medical applications 4.

This adoption pattern aligns with research indicating that healthcare institutions often prefer open-source models due to concerns about transparency and the need for customizable solutions tailored to specific clinical workflows 5.

Recent evaluations demonstrate that open-source AI models can match the performance of leading proprietary systems in solving complex medical cases, while being more accessible due to lower development costs 6.

3️⃣ China is emerging as a leader in practical AI healthcare implementation

The scale of DeepSeek’s clinical deployment, 300 hospitals already using their LLMs for diagnostics and decision support, positions China as a rapidly advancing player in practical healthcare AI implementation.

This widespread adoption contrasts with the generally slower integration of AI in Western healthcare systems, where implementation faces challenges related to workflow complexity and EHR integration 1.

Chinese AI companies like DeepSeek are developing competitive AI capabilities at significantly lower costs—DeepSeek’s R1 model reportedly cost around $6 million to develop compared to over $100 million for comparable Western models 7.

The practical focus on clinical diagnostics and medical decision support reflects a strategic alignment with areas where AI can deliver immediate value, addressing the rising healthcare costs and efficiency challenges faced globally 8.

By moving quickly from AI development to actual hospital implementation, China is gaining valuable real-world data and experience that can further improve AI system performance in healthcare settings.

Recent DeepSeek developments

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