ArticleArthroplasty (London, England)2026
Evaluation of DeepSeek with a RAG-based knowledge base in preoperative assessment for arthroplasty to improve the decision-making accuracy of junior physicians.
Article in Arthroplasty (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPatients undergoing elective joint replacement are mostly elderly individuals with multiple chronic diseases. These patients have a higher risk, so preoperative assessment for joint replacement is vital for surgical safety but remains challenging for junior physicians. This study aimed to evaluate the decision-making performance of different versions of DeepSeek (Chat and R1) and their knowledge base (KB)-enhanced schemes in perioperative risk assessment and their impact on junior physicians' decision-making quality.
methodsA total of 292 anonymized medical records from joint replacement patients were retrospectively analyzed. A professional knowledge base was constructed using 21 perioperative management guidelines and retrieval-augmented generation (RAG) technology. The performance of four models (DeepSeek-Chat, DeepSeek-Chat + KB, DeepSeek-R1, and DeepSeek-R1 + KB) was compared in ASA scoring, RCRI scoring, surgical tolerance judgment, and risk prediction. These indicators are related to specific decisions in joint replacement surgery, including preoperative optimization before elective TKA/THA, mitigation of infection risk, planning of VTE prophylaxis, and indication for postponing elective arthroplasty, etc. Senior physicians evaluated model performance using a Likert scale. A 5 × 5 balanced Latin square crossover design was used to assess the impact of model assistance on junior physicians' clinical decision-making.
resultsNo significant difference was found in ASA scoring accuracy (p > 0.05). For RCRI scoring and risk stratification, DeepSeek-R1 models (79.1%-79.7%) outperformed Chat models (56.5%-66.0%, p < 0.05). In surgical tolerance assessment, DeepSeek-R1 + KB showed relatively better performance, with significantly better predictive precision (p = 0.0284) and a lower missed diagnosis rate (p = 0.005) compared to the Chat models. The R1 series also showed higher predictive efficiency (87.2%-86.4% vs. 63.6%-61.8%, p < 0.05). The R1 models showed better performance than Chat models in medical comprehension, retrieval accuracy, and reasoning logic. After adjusting for physician variability and patient case mix, the DeepSeek-R1 + KB showed a statistically significant improvement over independent decision-making (OR = 1.58, p = 0.004), as well as the DeepSeek-R1 and Chat series.
conclusionThe DeepSeek-R1 model, with chain-of-thought reasoning, shows a marginal advantage over the Chat version in preoperative assessment for joint replacement. As a cost-effective clinical auxiliary tool, its integration with a professional knowledge base scheme can optimize the decision-making quality of junior physicians, particularly in terms of providing helpful and comprehensive decision support and providing novel insights for individualized assessment in the context of precision medicine.
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