China-based AI startup DeepSeek has taken a decisive step toward improving the accuracy and safety of its Medical AI systems. The company has started recruiting skilled interns to manually label sensitive clinical data, marking a human-in-the-loop approach to reducing risks tied to AI-generated medical recommendations. The initiative comes as DeepSeek ramps up its ambition to expand AI integration in hospital systems, particularly in diagnostic support and prescription automation. With growing scrutiny over AI safety in medicine, the company’s decision signals both urgency and innovation.
Skilled Interns to Power Safer AI Training
DeepSeek recently posted job openings on Chinese hiring platforms, revealing plans to hire interns with a unique mix of medical and technical expertise. The roles demand senior medical students or candidates with master’s degrees, especially those with experience in Python coding and prompt engineering for AI models.
The job pays around $70 per day and is currently limited to Beijing-based applicants. Interns will label clinical data by hand, enabling DeepSeek’s systems to learn from verified medical knowledge rather than relying solely on synthetic training sources. This manual process aims to increase reliability in AI-generated outputs, especially in high-stakes clinical use cases.
DeepSeek AI Already Used in 300 Hospitals
As of March 2025, at least 300 medical institutions in China have adopted DeepSeek’s Medical AI systems. The tools support physicians by automating parts of the diagnostic process and drafting preliminary prescriptions. These systems are powered by large language models (LLMs) tailored to China’s medical standards and language nuances.
The company believes that integrating manually labeled clinical data will help address one of the most pressing challenges in AI: hallucinations. These occur when language models produce information that sounds plausible but is factually incorrect. In a medical context, such errors can have dangerous consequences. By relying on human-reviewed data, DeepSeek is pushing for greater accuracy and trust in its systems.
AI Safety Under Growing Scrutiny
DeepSeek’s strategy also appears to respond to increased attention on AI safety in healthcare. In May, researchers warned that without proper validation, AI-generated medical suggestions could pose serious risks to patient safety. While the technology holds promise, experts emphasized the need for rigorous oversight and improvement.
By involving trained interns in the data-labeling process, DeepSeek is building a layer of accountability into its AI pipeline. The company expects this approach to reduce the frequency of hallucinations and improve the quality of medical responses generated by its models. The goal is not just to enhance performance, but to meet the growing demand for safe, factual AI tools in hospitals.
Human-in-the-Loop: A Path to Smarter Systems
The decision to incorporate human-labeled clinical data is part of a broader trend in the AI industry: blending machine learning with human expertise. DeepSeek’s human-in-the-loop model could serve as a blueprint for others building Medical AI solutions. Instead of relying only on algorithms to interpret complex health data, the system learns directly from verified, context-specific input.
By embedding human insight into the foundation of its models, DeepSeek aims to deliver not just smarter AI but safer and more dependable tools for real-world clinical use. The company’s current hires will play a critical role in shaping the model’s understanding of medical nuance, terminology, and diagnostic reasoning.
DeepSeek’s move to reinforce its Medical AI systems with manually labeled clinical data highlights a serious commitment to both innovation and safety. With real-world deployments already underway in hundreds of hospitals, the company is now sharpening its focus on reliability. In a landscape where AI mistakes can be costly, DeepSeek’s human-centered strategy offers a promising step toward smarter, safer medical technologies.
About The Author
