DeepSeek’s impact on thoracic surgeons’ work patterns—past, present and future

On January 27, 2025, the release of the new open-source large language model (LLM), DeepSeek, caused a global sensation. Humans have been working on developing artificial intelligence (AI) capable of natural language processing (NLP) and LLM is the biggest breakthrough to date. Even before the emergence of DeepSeek, LLMs had already demonstrated their vast potential in the medical field. The advantage of DeepSeek lies in its ability to achieve performance comparable to (or perhaps even superior to) top-tier closed-source LLMs like OpenAI at an extremely low cost—a level of performance once considered exclusive to proprietary LLMs. Given the long-standing advantages of open-source LLMs in terms of flexibility, cost-effectiveness, and transparency, the success of DeepSeek seems to signal that the medical community is one step closer to the “AI era”. Thoracic surgery is a discipline that has long been intertwined with AI. Twenty years ago, computer-aided diagnosis (CAD) was already being used in the diagnosis and treatment of pulmonary nodules (1). In this article, we will discuss the opportunities and challenges that thoracic surgery will face in the “DeepSeek era”.

In the “pre-DeepSeek era”, AI had already permeated the entire process of diagnosis and treatment in thoracic surgery, spanning preoperative, intraoperative, and postoperative stages. With the advancement of machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), AI has become deeply involved in the interpretation of imaging results in thoracic surgery (2). In cases where historical imaging data is insufficient, AI has even demonstrated greater accuracy than human doctors (3). AI has also gradually been applied to the interpretation of pathological results. While insufficient tissue samples often limit the practical application of immunohistochemistry and genetic testing, AI can make precise judgments even with limited samples (4). Furthermore, AI appears to outperform intraoperative frozen sections in identifying spread through air spaces, which directly impacts the surgical approach for early-stage non-small cell lung cancer (NSCLC) (5).

Meanwhile, AI has gradually become involved in the surgical procedures of thoracic surgery. By preoperatively delineating tumor boundaries, AI has reduced the time required to locate tumors during surgery while ensuring complete resection margins (R0 resection). Recently, the results of JCOG0802 and CALGB140503 have significantly elevated the status of segmentectomy as a curative surgery for early-stage NSCLC. Accurately identifying intersegmental planes during segmentectomy is a challenging task, but augmented reality (AR) and virtual reality (VR) technologies can clearly expose anatomical structures, greatly simplifying this process (6,7).

These achievements, however, do not mean that thoracic surgery has entered the “AI era”. First, the high economic burden has long been a concern for the adoption of AI. Second, many AI models are trained on small-scale, highly specialized datasets, which can lead to “overfitting” and diminish their performance in real-world applications. Most importantly, AI still cannot directly participate in medical decision-making. Medical decision-making is a complex reasoning task, where the reasoning process is as important as the outcome. For doctors, relying on an AI that only provides answers without transparency is unimaginable. Some closed-source LLMs (e.g., OpenAI) can provide reasoning processes, but the “black box” nature of these models still lacks transparency and persuasiveness at a technical level.

DeepSeek is set to change this. DeepSeek can construct a complete chain of thought for any response, demonstrating powerful reasoning capabilities. As an open-source LLM, DeepSeek’s reasoning abilities can be widely validated at a technical level. Therefore, doctors can confidently refer to DeepSeek’s recommendations during decision-making without worrying about the transparency or safety of the advice’s origin. Additionally, DeepSeek can extensively access large medical public databases and stay updated with the latest medical advancements, significantly enhancing the reliability of its recommendations. While DeepSeek cannot replace human doctors in decision-making, it can make decisions more accurate and faster. This is particularly valuable in critical situations, such as clinical decision-making for patients with severe conditions like heart valve rupture or aortic dissection, where time is of the essence.

DeepSeek can also assist in thoracic surgical procedures. By accurately interpreting imaging results and performing preoperative 3D reconstruction of the surgical field’s anatomical structures, AI can help surgeons plan surgeries more precisely before the operation (8). Beyond preoperative planning, DeepSeek can effectively assist in managing postoperative complications. Thoracic surgeries, especially complex cardiac surgeries, still have a high incidence of postoperative complications. DeepSeek can comprehensively assess a patient’s overall condition and various test indicators, providing early warnings of complication risks even before surgery (9). Moreover, DeepSeek’s NLP capabilities enable it to answer frequently asked questions in real time, greatly aiding patient education before and after surgery (10).

Using AI to process experimental data in medical research is no longer news (11). However, DeepSeek can do much more. DeepSeek can efficiently read literature, helping researchers overcome language barriers when reading non-native language publications. Novelty is a critical metric in evaluating medical research. By extensively reviewing literature, DeepSeek can quickly identify potential research hotspots and present them to researchers. Similarly, DeepSeek can shorten the lengthy preliminary processes required for writing meta-analyses and systematic reviews, such as literature collection and screening, generating forest plots, and conducting heterogeneity analyses. In summary, DeepSeek allows researchers to focus on the “research” itself rather than being overwhelmed by massive amounts of data and text. Multidisciplinary collaboration is the future trend in medical research, and achieving this requires a robust public platform. The success of the BioChatter platform (12) demonstrates that the open, diverse, and inclusive community environment of open-source LLMs has significantly contributed to the advancement of medical research. We believe DeepSeek will perform even better in the future.

The future: how DeepSeek will shape thoracic surgery

The transition from experiential medicine to precision medicine is a key goal in the clinical practice of thoracic surgery. By integrating electronic medical records, imaging results, and preoperative 3D anatomical reconstructions, DeepSeek may provide the optimal surgical plan for each patient. The field of adjuvant therapy for malignant tumors is an even more intriguing direction. Utilizing DL based on cold-start data, DeepSeek can effectively make precise judgments with limited data. Based on genomic results, DeepSeek can quickly identify the gene sequences of lung cancer cells, enabling personalized targeted therapies for lung cancers with rare genetic mutations (13). Additionally, DeepSeek can monitor changes in circulating tumor DNA during follow-up, predict drug resistance trends, and adjust treatment strategies in a timely manner.

“Intelligent surgery” represents the future trend in thoracic surgery. Robot-assisted thoracic surgery (RATS) has already been widely adopted in thoracic surgery and shows potential to replace traditional video-assisted thoracoscopic surgery (VATS) (14). The integration of DeepSeek with RATS may further optimize surgical workflows. Through AR technology, AI can clearly delineate tumor boundaries, display the course of blood vessels and nerves, and locate lymph nodes during lung cancer surgery. The DL capabilities of AI enable it to rapidly learn from vast amounts of surgical video data, achieving the skill level of experienced human surgeons in a remarkably short time. Certain thoracic tumors, such as locally advanced esophageal cancers invading the bronchi or major blood vessels, pose significant resection challenges (15). However, VR technology allows surgeons to “rehearse” surgical steps and predict outcomes.

DeepSeek will also profoundly transform the training model for thoracic surgeons. By connecting to various large medical databases, trainees can easily access and learn from the latest surgical videos and medical guidelines. More importantly, AI offers a shortcut for developing surgical skills, addressing the conflict between training authenticity and safety. While allowing beginners to perform real surgeries may be risky, simulating lung cancer resections using VR technology is entirely safe.

Riedemann et al. (16) noted that compared to closed-source LLMs, the security of open-source LLMs can be promptly inspected. However, before formally applying DeepSeek to medicine, it is necessary to establish effective laws and regulations to clarify liability (17). No matter how astonishing technological advancements may be, the final medical decisions should always come from human doctors rather than AI like DeepSeek (18). We must also remember that in certain medical scenarios, such as palliative care for patients with advanced thoracic tumors, human empathy and subjective emotions remain irreplaceable (19).

Conclusions

The emergence of DeepSeek is another milestone in the development of AI. The low cost and high efficiency of DeepSeek make the expansion of AI, especially LLM, in thoracic surgery a reality. Precision, efficiency, and innovation will be the themes of thoracic surgery in the DeepSeek era. However, we should still carefully evaluate whether the time is ripe for DeepSeek to be fully operational and carefully address potential ethical and safety issues.

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: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025b-04/coif). The 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.

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/.

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