Keywords: Artificial intelligence (AI); DeepSeek; catfish effect; open source; medical applications                                                                During the 2025 Chinese Spring Festival, a topic that garnered widespread attention was DeepSeek. On January 20, the Hangzhou-based DeepSeek company released its latest large language model, DeepSeek-R1. This release sent shockwaves through the technology sector and attracted attention from top scientific journals such as Nature and Science (1,2). With its powerful performance and open-source characteristics, DeepSeek-R1 has created substantial pressure on existing artificial intelligence (AI) competitors, exemplifying the “catfish effect” in the AI domain. This concept originates from a classical management theory: Norwegian fishermen placed catfish, a natural predator, in sardine transport tanks, significantly reducing mortality rates by triggering the sardines’ survival instincts. By analogy, in other fields, the introduction of strong competitors often activates industry innovation dynamics. DeepSeek’s emergence has injected new momentum into the AI field, driving rapid technological iteration and innovation.

DeepSeek is an AI platform based on deep learning and natural language processing, featuring core products DeepSeek-R1 and DeepSeek-V3 models. Using efficient Mixture of Experts (MoE) architecture and multimodal data fusion capabilities, it achieves performance comparable to OpenAI's GPT-4o-mini while significantly reducing training costs .

In healthcare applications, DeepSeek rapidly extracts valuable information from massive datasets, providing intelligent information retrieval and analysis solutions  that:

- Assist physicians with diagnosis and treatment - Optimize doctor-patient communication - Improve medical efficiency - Supplement professional knowledge - Provide humanized medical interactions - Identify potential blind spots 

DeepSeek enhances clinical decision-making efficiency, supports scientific research, optimizes patient management  and assists patients with medical decision-making .

However, the platform faces challenges including data quality issues, algorithm stability and accuracy concerns, multimodal data fusion problems, lack of automated information collection, and dynamic update delays .

Future developments may bring breakthroughs in personalized medicine, telemedicine, and public health management .