Artificial intelligence has increasingly become integral to radiology, offering new methods to enhance diagnostic accuracy and efficiency. Among the latest advancements, DeepSeek stands out as a disruptive force. Developed by a Chinese AI startup, it introduces open-weight models designed to optimise computational efficiency and problem-solving. DeepSeek’s emergence has generated excitement within engineering and biomedical fields while also raising economic and ethical questions. Its novel approach to AI model training, reasoning and accessibility suggests a significant shift in how AI is integrated into radiology.
DeepSeek’s ability to challenge established AI models has drawn considerable attention. Unlike many proprietary models, its open-weight structure allows researchers to scrutinise its internal workings. This transparency sets it apart from competitors, fostering greater collaboration in AI development. However, while its training processes can be examined, its training data and source code remain undisclosed, raising questions about its long-term accessibility and ethical considerations.
Technological Innovations of DeepSeek
DeepSeek has developed a range of AI models, including DeepSeek-V3 and DeepSeek-R1, which aim to compete with major proprietary models such as OpenAI’s GPT-4o. A key differentiator of DeepSeek is its open-weight structure, allowing researchers and developers to examine its training processes. However, the training data and source code remain proprietary. The model employs a multistage training approach, beginning with a cold-start phase and progressing through reinforcement learning steps that refine outputs based on human feedback. This structured training process enhances DeepSeek’s ability to generate consistent and accurate responses while improving its problem-solving capabilities.
To enhance efficiency and reduce computational costs, DeepSeek incorporates FP8 training and a Mixture of Experts framework. FP8 training minimises processing requirements by reducing data precision, while the Mixture of Experts approach enables the model to allocate resources dynamically for improved problem-solving. Unlike conventional deep learning approaches, DeepSeek’s method of dividing complex problems into smaller, more manageable components optimises computational resources, reducing both cost and power requirements.
Additionally, DeepSeek’s Multi-head Latent Attention mechanism optimises inference efficiency, ensuring scalability across different model sizes. This technology transforms key-value cache data into latent vectors, significantly reducing the computational overhead required for processing large-scale AI tasks. As a result, DeepSeek models require far fewer resources than comparable AI systems, making them more accessible to organisations with limited computing infrastructure. The efficiency of DeepSeek is further demonstrated by the fact that a single server equipped with only eight H200 GPUs can effectively run the full version of DeepSeek-R1, a significant improvement in AI model deployment.
DeepSeek’s Role in Radiology
DeepSeek’s capability for complex reasoning is particularly relevant to radiology, where AI must synthesise diverse data sources to aid diagnosis. The model’s chain-of-thought reasoning enables structured analytical responses, reducing reliance on manually constructed prompts. DeepSeek also demonstrates self-reflection and error correction, which are crucial in medical applications where accuracy is paramount.
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One of DeepSeek’s most notable strengths is its ability to guide users through logical reasoning when interpreting medical data. Unlike some AI models that generate immediate responses, DeepSeek first considers the problem, formulates a reasoning process, and only then provides an output. This structured approach is beneficial for radiologists, who require clear, evidence-based conclusions when assessing medical imaging and reports.
In performance evaluations, DeepSeek-R1 was tested on a disease classification problem using synthetic radiology reports. Results showed that it outperformed some models, such as Llama-3.3-70B, but did not demonstrate a clear advantage over GPT-4o. However, its open-weight nature allows local implementation, making it feasible for healthcare organisations to integrate AI without external dependencies. The ability to deploy AI models locally enhances data security and customisation potential, offering a viable alternative to proprietary AI solutions. By running DeepSeek on in-house infrastructure, healthcare providers can maintain greater control over patient data while leveraging AI for diagnostic assistance.
Challenges and Ethical Considerations
Despite its potential, DeepSeek faces significant challenges, particularly concerning data privacy and regulatory compliance. Some governments have banned its use on official devices due to security concerns. While its open-weight design supports transparency, the lack of publicly available training data raises questions about bias and accountability. Ensuring that AI applications in radiology adhere to ethical and legal standards will require ongoing scrutiny.
Another concern is the verbosity of DeepSeek’s responses. While its reasoning capabilities enhance explainability, excessively detailed outputs may hinder usability for radiologists who require concise, actionable insights. This raises the question of whether the additional detail provided by DeepSeek’s reasoning adds genuine value to clinical decision-making or whether it introduces unnecessary complexity. Balancing clarity and detail will be essential for maximising the practical benefits of AI-driven radiology.
Furthermore, AI models like DeepSeek require rigorous evaluation across multiple dimensions, including trust, safety and diagnostic accuracy. Current assessments remain limited in scope, highlighting the need for further studies to validate DeepSeek’s effectiveness in clinical settings. The model has yet to undergo large-scale clinical trials, meaning that its full potential and limitations remain uncertain. Additional research will be needed to determine whether DeepSeek can consistently outperform existing AI tools across a broad range of radiological tasks.
DeepSeek represents a major advancement in AI-driven radiology, providing an open-weight alternative that balances computational efficiency with advanced reasoning capabilities. Its ability to operate locally makes it an attractive option for healthcare institutions seeking greater control over AI deployment. However, concerns regarding privacy, usability and regulatory compliance must be addressed before DeepSeek can achieve widespread clinical adoption.
As DeepSeek and other large language models, both proprietary and open, continue to develop, there is a growing need for a collaborative approach to their integration into clinical domains. This process must involve a range of stakeholders, including AI developers, ethicists, radiologists and end-users, to ensure that these models effectively meet real-world needs while adhering to ethical and societal standards.
Source: Radiology Advances
Image Credit: Pixabay
