Comparison of DeepSeek-V3.1 and ChatGPT-5 in Multidisciplinary Team Decision-making for Colorectal Liver Metastases

ZHANG Yangzi; XU Ting; GAO Zhaoya; SI Zhenduo; XU Weiran

doi:10.11999/JEIT250849

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ZHANG Yangzi, XU Ting, GAO Zhaoya, SI Zhenduo, XU Weiran. Comparison of DeepSeek-V3.1 and ChatGPT-5 in Multidisciplinary Team Decision-making for Colorectal Liver Metastases[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250849

Citation:

ZHANG Yangzi, XU Ting, GAO Zhaoya, SI Zhenduo, XU Weiran. Comparison of DeepSeek-V3.1 and ChatGPT-5 in Multidisciplinary Team Decision-making for Colorectal Liver Metastases[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250849

Citation:

ZHANG Yangzi, XU Ting, GAO Zhaoya, SI Zhenduo, XU Weiran. Comparison of DeepSeek-V3.1 and ChatGPT-5 in Multidisciplinary Team Decision-making for Colorectal Liver Metastases[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250849

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Comparison of DeepSeek-V3.1 and ChatGPT-5 in Multidisciplinary Team Decision-making for Colorectal Liver Metastases

doi: 10.11999/JEIT250849 cstr: 32379.14.JEIT250849

1.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
2.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
3.
Department of Gastrointestinal Surgery, Peking University Shougang Hospital, Beijing 100144, China
4.
Department of Hepatobiliary and Pancreatic Surgery, Peking University Shougang Hospital, Beijing 100144, China
5.
Department of Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

Funds: The Ministry of Education Industry-University Cooperative Education Project (2506193449); The National Key Laboratory of Virtual Reality Technology and Systems at Beihang University (VRLAB2025C15); Beijing Tiantan Hospital Institutional Research Fund (TYGL202402)

Received Date: 2025-09-01
Accepted Date: 2025-11-12
Rev Recd Date: 2025-11-06

Available Online: 2025-11-18

Abstract

Abstract

Objective ColoRectal Cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Approximately 25–50% of patients with CRC develop liver metastases during the course of their disease, which increases the disease burden. Although the MultiDisciplinary Team (MDT) model improves survival in ColoRectal Liver Metastases (CRLM), its broader implementation is limited by delayed knowledge updates and regional differences in medical standards. Large Language Models (LLMs) can integrate multimodal data, clinical guidelines, and recent research findings, and can generate structured diagnostic and therapeutic recommendations. These features suggest potential to support MDT-based care. However, the actual effectiveness of LLMs in MDT decision-making for CRLM has not been systematically evaluated. This study assesses the performance of DeepSeek-V3.1 and ChatGPT-5 in supporting MDT decisions for CRLM and examines the consistency of their recommendations with MDT expert consensus. The findings provide evidence-based guidance and identify directions for optimizing LLM applications in clinical practice. Methods Six representative virtual CRLM cases are designed to capture key clinical dimensions, including colorectal tumor recurrence risk, resectability of liver metastases, genetic mutation profiles (e.g., KRAS/BRAF mutations, HER2 amplification status, and microsatellite instability), and patient functional status. Using a structured prompt strategy, MDT treatment recommendations are generated separately by the DeepSeek-V3.1 and ChatGPT-5 models. Independent evaluations are conducted by four MDT specialists from gastrointestinal oncology, gastrointestinal surgery, hepatobiliary surgery, and radiation oncology. The model outputs are scored using a 5-point Likert scale across seven dimensions: accuracy, comprehensiveness, frontier relevance, clarity, individualization, hallucination risk, and ethical safety. Statistical analysis is performed to compare the performance of DeepSeek-V3.1 and ChatGPT-5 across individual cases, evaluation dimensions, and clinical disciplines. Results and Discussions Both LLMs, DeepSeek-V3.1 and ChatGPT-5, show robust performance across all six virtual CRLM cases, with an average overall score of ≥ 4.0 on a 5-point scale. This performance indicates that clinically acceptable decision support is provided within a complex MDT framework. DeepSeek-V3.1 shows superior overall performance compared with ChatGPT-5 (4.27±0.77 vs. 4.08±0.86, P=0.03). Case-by-case analysis shows that DeepSeek-V3.1 performs significantly better in Cases 1, 4, and 6 (P=0.04, P<0.01, and P =0.01, respectively), whereas ChatGPT-5 receives higher scores in Case 2 (P<0.01). No significant differences are observed in Cases 3 and 5 (P=0.12 and P=1.00, respectively), suggesting complementary strengths across clinical scenarios (Table 3). In the multidimensional assessment, both models receive high scores (range: 4.12$ \sim $4.87) in clarity, individualization, hallucination risk, and ethical safety, confirming that readable, patient-tailored, reliable, and ethically sound recommendations are generated. Improvements are still needed in accuracy, comprehensiveness, and frontier relevance (Fig. 1). DeepSeek-V3.1 shows a significant advantage in frontier relevance (3.90±0.65 vs. 3.42±0.72, P=0.03) and ethical safety (4.87±0.34 vs. 4.58±0.65, P= 0.03) (Table 4), indicating more effective incorporation of recent evidence and more consistent delivery of ethically robust guidance. For the case with concomitant BRAF V600E and KRAS G12D mutations, DeepSeek-V3.1 accurately references a phase III randomized controlled study published in the New England Journal of Medicine in 2025 and recommends a triple regimen consisting of a BRAF inhibitor + EGFR monoclonal antibody + FOLFOX. By contrast, ChatGPT-5 follows conventional recommendations for RAS/BRAF mutant populations-FOLFOXIRI+bevacizumab-without integrating recent evidence on targeted combination therapy. This difference shows the effect of timely knowledge updates on the clinical value of LLM-generated recommendations. For MSI-H CRLM, ChatGPT-5’s recommendation of “postoperative immunotherapy” is not supported by phase III evidence or existing guidelines. Direct use of such recommendations may lead to overtreatment or ineffective therapy, representing a clear ethical concern and illustrating hallucination risks in LLMs. Discipline-specific analysis shows notable variation. In radiation oncology, DeepSeek-V3.1 provides significantly more precise guidance on treatment timing, dosage, and techniques than ChatGPT-5 (4.55±0.67 vs. 3.38±0.91, P<0.01), demonstrating closer alignment with clinical guidelines. In contrast, ChatGPT-5 performs better in gastrointestinal surgery (4.48±0.67 vs. 4.17 ±0.85, P=0.02), with experts rating its recommendations on surgical timing and resectability as more concise and accurate. No significant differences are identified in gastrointestinal oncology and hepatobiliary surgery (P=0.89 and P=0.14, respectively), indicating comparable performance in these areas (Table 5). These findings show a performance bias across medical sub-specialties, demonstrating that LLM effectiveness depends on the distribution and quality of training data. Conclusions Both DeepSeek-V3.1 and ChatGPT-5 demonstrated strong capabilities in providing reliable recommendations for CRLM-MDT decision-making. Specifically, DeepSeek-V3.1 showed notable advantages in integrating cutting-edge knowledge, ensuring ethical safety, and performing in the field of radiation oncology, whereas ChatGPT-5 excelled in gastrointestinal surgery, reflecting a complementary strength between the two models. This study confirms the feasibility of leveraging LLMs as “MDT collaborators”, offering a readily applicable and robust technical solution to bridge regional disparities in clinical expertise and enhance the efficiency of decision-making. However, model hallucination and insufficient evidence grading remain key limitations. Moving forward, mechanisms such as real-world clinical validation, evidence traceability, and reinforcement learning from human feedback are expected to further advance LLMs into more powerful auxiliary tools for CRLM-MDT decision support.
- Large Language Models (LLMs),
- DeepSeek-V3.1,
- ChatGPT-5,
- ColoRectal Liver Metastases (CRLM),
- MultiDisciplinary Team (MDT)

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References(21)

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