The rise of DeepSeek: technology calls for the “catfish effect”
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.
Cost-effectiveness and efficiency: DeepSeek’s technological breakthroughs
DeepSeek-R1’s development costs are significantly lower than comparable Western products, an advantage that has distinguished it in the global AI market. Its core technology, the “Mixture-of-Experts” architecture, optimizes the training process by reducing parameter quantities and chip requirements, thereby substantially lowering costs. Additionally, the Multi-head Latent Attention mechanism enables the model to store more data while occupying less memory. These innovations significantly enhance model efficiency, making it more competitive in resource-constrained environments.
Innovation under restrictions: the birth context of DeepSeek
Against the backdrop of U.S. restrictions on high-performance chip exports to China, DeepSeek’s emergence demonstrates China’s autonomous innovation capabilities in AI. Through algorithm optimization and independent research and development, DeepSeek has successfully overcome hardware limitations to achieve technological breakthroughs. This achievement not only showcases China’s strength in the AI field but also provides new perspectives for global AI technological development. We believe that China’s advancements in AI will, in turn, stimulate AI development in the U.S., particularly through open-source products like DeepSeek. For the collective advancement of human technology, we unequivocally advocate for openness and oppose restrictions.
Local deployment and data privacy: DeepSeek’s unique advantages
DeepSeek-R1’s support for local deployment offers unique value in terms of privacy protection. Researchers can deploy the model on local systems, thereby maintaining complete control over their data and research outcomes. This innovative design is particularly significant for disciplines involving sensitive data, such as medical research.
The power of open source: promoting transparency and collaboration in AI research
The open-source nature of DeepSeek-R1 makes its reasoning processes transparent to researchers, thereby enhancing model interpretability. This transparency not only facilitates understanding of the model’s decision-making mechanisms but also enables potential model improvements. The open-source model simultaneously promotes collaboration within the global research community, allowing competitors to iteratively optimize based on DeepSeek’s methods, thus advancing the entire field.
AI applications in medicine: potential and challenges
AI (including DeepSeek) has demonstrated significant potential in medical applications, particularly in driving paradigm shifts and innovative thinking in research. For instance, AI can assist physicians in early diagnosis and surgical planning through analysis of medical imaging data (3). AI can also help researchers write code, optimize algorithms, and even revise and refine academic papers (4). Furthermore, AI can accelerate research progress by analyzing vast literature databases to help researchers quickly access the latest research developments. The development process of this article itself illustrates the value of AI assistance—a Chinese draft was repeatedly refined through DeepSeek until the final version was completed. This experience stands in stark contrast to earlier approaches: previously, one may need to hire professional editors for language refinement, which was time-consuming, expensive, and communication-intensive. Today, AI technology not only provides an “omniscient partner” available around the clock but also completes dozens of iterative optimizations with remarkable patience. Had someone predicted this scenario 10 years ago, I would have dismissed it as fantasy.
However, AI still faces numerous challenges in clinical applications. First, data bias and generalization issues have not been fully resolved. Training data biases in models like DeepSeek-R1 and ChatGPT may influence clinical research outcomes. Second, AI has limited adaptability in complex contexts and lacks human emotional capacity and judgment. For example, in scenarios such as end-of-life care or mental health interventions, AI performance remains suboptimal. To standardize AI clinical applications, frameworks such as CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) have emerged, providing new reporting standards for evaluating clinical trials with AI interventions (5).
Embracing AI, welcoming competition, maintaining vigilance
DeepSeek’s emergence has injected new vitality into the AI field, driving rapid technological advancement. This “catfish effect” not only stimulates competition but also provides new momentum for global AI technological development. However, AI technology is still in the developmental stage and requires human guidance and regulation. We should actively embrace AI while remaining vigilant, ensuring that its applications in medicine and other fields truly benefit humanity.
Acknowledgments
None.
Footnote
Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Thoracic Disease. The article did not undergo external peer review.
Funding: None.
Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025b-02/coif). The author has no conflicts of interest to declare.
Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
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- Dreyer J. China made waves with Deepseek, but its real ambition is AI-driven industrial innovation. Nature 2025;638:609-11. [Crossref] [PubMed]
- Faes L, Wagner SK, Fu DJ, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Lancet Digit Health 2019;1:e232-42. [Crossref] [PubMed]
- Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care 2023;27:75. [Crossref] [PubMed]
- Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2020;2:e537-48. [Crossref] [PubMed]
