Preliminary evaluation of DeepSeek-R1 and GPT-5.3 in selected PET/CT clinical scenarios: patient preparation, report interpretation, and diagnostic reasoning.

Objective: To evaluate the performance of DeepSeek (R1 version), an open-source large language model, in three core clinical scenarios: answering patients' common questions, interpreting PET/CT reports with follow-up inquiries, and diagnosing complex cases, and comparison with GPT-5.3, to verify the clinical applicability of DeepSeek-R1 as an alternative AI assistant. Methods: A total of 39 standardized tasks were assigned to both models, including responding to 15 frequently asked questions about [ Results: Across the 39 tasks, DeepSeek-R1 achieved 94.9% appropriateness and 100% helpfulness. Specifically, 91.7% of responses to follow-up inquiries about tumor staging or treatment were rated empathetic. However, 7.7% of regenerated responses showed substantial inconsistencies, primarily in tumor staging, and only 37% of cited references were fully valid, with 11.1% being invalid. GPT-5.3 exhibited equivalent core performance to DeepSeek-R1 with 94.9% appropriateness and 100% helpfulness, a slightly lower substantial inconsistency rate (5.1%), favorable reference validity (33% fully valid, 7.4% invalid), but a notably lower empathy score (66.7%) for follow-up inquiries. McNemar tests showed identical appropriateness ( Conclusion: DeepSeek-R1 and GPT-5.3 have complementary strengths but similar reference hallucination issues and cannot replace clinicians. DeepSeek-R1 is a cost-effective auxiliary tool, with future optimization needed for consistency, diagnostic accuracy and reference validity.