DeepSeek: the “Watson” to doctors—from assistance to collaboration
DeepSeek is an artificial intelligence (AI) platform built on deep learning and natural language processing (NLP) technologies. Its core products include the DeepSeek-R1 and DeepSeek-V3 models. Leveraging an efficient Mixture of Experts (MoE) architecture, multimodal data fusion capabilities, and significantly reduced training costs (over 90% lower than comparable models), DeepSeek achieves performance on par with OpenAI’s GPT-4o-mini. Through cutting-edge NLP techniques, DeepSeek can rapidly and accurately extract valuable insights from massive datasets, providing users with intelligent information retrieval and analytical solutions. These capabilities unlock new possibilities in healthcare, offering efficient support to both doctors and patients.
In the medical field, clinicians are akin to the legendary detective Sherlock Holmes, while DeepSeek plays the role of “John Watson”—not just an assistant, but also a complement to their thinking and a bridge to humanized care. With its powerful data analysis and reasoning capabilities, DeepSeek helps doctors identify potential “blind spots”, provides comprehensive diagnostic recommendations, optimizes doctor-patient communication, and enhances medical efficiency. Specifically, DeepSeek offers the following benefits: (I) supplementing professional knowledge: through deep learning and multimodal data processing, DeepSeek delivers big data-driven insights, enabling doctors to better understand complex medical conditions. (II) Humanizing healthcare interactions: by enhancing patient education and doctor-patient communication, DeepSeek facilitates more effective interactions, improving the overall healthcare experience. (III) Identifying blind spots: much like Watson, who is often humorously credited with “spotting the blind spots”, DeepSeek’s comprehensive algorithms help doctors uncover details that might otherwise be overlooked. For instance, in diagnosing rare diseases or analyzing complex cases, DeepSeek can identify potential disease features or risk factors, offering doctors a supplementary perspective. (IV) Documentation and organization: just as Watson documented Holmes’ deductive processes for posterity, DeepSeek generates medical record templates and follow-up plans, helping doctors efficiently manage patient information while ensuring data integrity and traceability. Thus, DeepSeek is not merely an intelligent assistant but a collaborative partner in medical practice.
Doctors can utilize DeepSeek for diagnostic and therapeutic assistance while ensuring information privacy protection. Hospitals can also deploy DeepSeek locally and customize its development, training AI models with real-world data to improve the accuracy of diagnoses and treatment recommendations. Below are specific application scenarios for DeepSeek in medical practice: (I) enhancing clinical decision-making efficiency: in clinical settings, doctors can input patient symptoms, test results, and other relevant information to receive diagnostic suggestions and treatment plans from DeepSeek. For instance, when dealing with complex cases, DeepSeek integrates patient medical history and the latest medical research to provide comprehensive decision support. It can also tailor personalized treatment plans based on individual patient characteristics and needs. Additionally, DeepSeek’s data collection and processing capabilities can automatically generate structured medical record drafts, significantly reducing the documentation burden on doctors. (II) Supporting research and academia: DeepSeek can rapidly retrieve the latest medical literature and clinical guidelines, assisting doctors in conducting literature reviews and analyzing research data. This enables them to identify solutions for challenging cases and even propose new scientific questions and research directions. Through human-AI collaboration, doctors can expand their cognitive boundaries, while DeepSeek’s “white-box” interpretability design enhances their understanding of AI reasoning processes and facilitates learning from its insights. (III) Optimizing patient management: DeepSeek can generate personalized patient education programs, helping doctors communicate more effectively with patients and improve treatment adherence. Through its intelligent analysis, doctors can more efficiently manage patient follow-ups and rehabilitation plans, ensuring continuity and effectiveness of care.
At the same time, doctors should actively guide patients in using DeepSeek to improve healthcare efficiency and health management capabilities. This includes: (I) assisting healthcare decision-making: patients can use DeepSeek to preliminarily understand potential diseases corresponding to their symptoms, as well as relevant tests and treatment recommendations. For example, before visiting a doctor, patients can use DeepSeek to interpret lab reports and gain initial diagnostic insights. Additionally, patients can access health science knowledge through DeepSeek to enhance their health literacy and even evaluate the expertise of potential doctors and their suitability for the condition. (II) Optimizing doctor-patient communication: by using DeepSeek, patients gain a deeper understanding of their conditions, enabling more efficient communication with doctors and reducing unnecessary misunderstandings and anxiety. For instance, DeepSeek can generate condition summaries to help patients clearly describe their symptoms to doctors. It can also explain medical terminology, allowing patients to better comprehend diagnoses and treatment plans.
Despite its immense potential in healthcare applications, DeepSeek still faces several challenges that require further optimization: (I) data quality issues: the completeness and accuracy of medical data are critical factors influencing DeepSeek’s effectiveness. Irregular data entry and inconsistent collection standards may lead to biases in model analysis and diagnosis. To address this, DeepSeek needs to integrate professional medical databases (such as PubMed) and incorporate more high-quality cases and information to ensure data diversity and representativeness. Additionally, data cleaning and standardization processes should be implemented to enhance data quality. (II) Algorithm stability and accuracy: when dealing with rare diseases (e.g., those with an incidence rate of <1/100,000) or complex cases, the scarcity of relevant data may prevent DeepSeek from accurately identifying disease characteristics, resulting in unreliable diagnoses. To improve performance, DeepSeek can expand training datasets for rare diseases or adopt transfer learning techniques. Furthermore, rigorous testing and validation are necessary to ensure the reliability of model updates and optimizations in clinical practice. (III) Multimodal data fusion: healthcare involves diverse data types (e.g., images, text, physiological signals), but DeepSeek still faces technical bottlenecks in multimodal data fusion, limiting its ability to integrate data effectively for comprehensive diagnostic and treatment recommendations. To overcome this, hierarchical attention mechanisms (e.g., cross-modal attention weight allocation between images and text) can be employed, along with time-series modeling to capture dynamic features of disease progression. (IV) Lack of automated information collection: currently, DeepSeek relies on user-initiated input, creating barriers to usability. To reduce user burden and improve system accessibility, an active information collection approach is needed. For example, clinical decision tree models can be introduced to develop proactive questioning capabilities during doctor-patient interactions, guiding patients to provide key information and enhancing data collection efficiency. (V) Dynamic update delays: the integration of the latest clinical guidelines and research findings often requires manual review, leading to delays of 1–3 months, which may compromise the timeliness of the model. To address this, continuous learning mechanisms should be implemented, enabling the model to dynamically incorporate the most recent data.
In summary, DeepSeek has brought significant efficiency improvements and convenience to the healthcare sector. However, its application must be integrated with professional medical knowledge and real-world scenarios to ensure safety and accuracy. Looking ahead, DeepSeek is poised to make breakthroughs in personalized medicine, telemedicine, and public health management. By continuously improving data quality, privacy protection, technical optimization, ethical and legal compliance, and industry acceptance, DeepSeek will become a reliable partner for both doctors and patients, driving the intelligent transformation of healthcare. Just as Watson grew under Holmes’ guidance, DeepSeek will continue to refine itself in medical practice, ultimately achieving the leap from assistance to collaboration.
Acknowledgments
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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: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025b-03/coif). N.Z. serves as the Editor-in-Chief of Journal of Thoracic Disease. J.H. serves as the unpaid Executive Editor-in-Chief of Journal of Thoracic Disease. Y.L. is from Guangzhou KingMed Diagnostics Group Co., Ltd. The other authors have no conflicts of interest to declare.
Ethical Statement: The authors are 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.
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