LLMs在疾病诊断中的应用：DeepSeek-R1与O3 Mini在慢性健康状况下的比较研究
LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions
AI总结：
本研究比较了DeepSeek R1与O3 Mini在慢性病诊断中的性能，发现DeepSeek R1总体准确率更高（82% vs 75%），且在心理健康等领域达100%准确率，为LLM诊断系统提供了优劣评估与改进方向。
摘要：
大型语言模型（LLMs）通过增强疾病分类和临床决策，正在革新医学诊断领域。在本研究中，我们使用一个结构化的症状与诊断数据集，评估了两种基于LLM的诊断工具——DeepSeek R1和O3 Mini的性能。我们在疾病层面和类别层面评估了它们的预测准确性，以及其置信度分数的可靠性。DeepSeek R1在疾病层面的准确率达到76%，总体准确率为82%，优于O3 Mini的72%和75%。值得注意的是，DeepSeek R1在心理健康、神经疾病和肿瘤学领域表现卓越，准确率达到100%，而O3 Mini在自身免疫疾病分类中表现优异，准确率为100%。然而，两种模型在呼吸系统疾病分类上均表现不佳，DeepSeek R1的准确率仅为40%，O3 Mini为20%。此外，置信度分数分析显示，DeepSeek R1在92%的案例中提供了高置信度预测，而O3 Mini仅为68%。我们还讨论了关于偏见、模型可解释性和数据隐私的伦理考量，以确保LLMs负责任地整合到临床实践中。总体而言，我们的研究结果为基于LLM的诊断系统的优势和局限性提供了宝贵见解，并为AI驱动的医疗保健的未来改进提供了路线图。
英文摘要：
Large Language Models (LLMs) are revolutionizing medical diagnostics by enhancing both disease classification and clinical decision-making. In this study, we evaluate the performance of two LLM- based diagnostic tools, DeepSeek R1 and O3 Mini, using a structured dataset of symptoms and diagnoses. We assessed their predictive accuracy at both the disease and category levels, as well as the reliability of their confidence scores. DeepSeek R1 achieved a disease-level accuracy of 76% and an overall accuracy of 82%, outperforming O3 Mini, which attained 72% and 75% respectively. Notably, DeepSeek R1 demonstrated exceptional performance in Mental Health, Neurological Disorders, and Oncology, where it reached 100% accuracy, while O3 Mini excelled in Autoimmune Disease classification with 100% accuracy. Both models, however, struggled with Respiratory Disease classification, recording accuracies of only 40% for DeepSeek R1 and 20% for O3 Mini. Additionally, the analysis of confidence scores revealed that DeepSeek R1 provided high-confidence predictions in 92% of cases, compared to 68% for O3 Mini. Ethical considerations regarding bias, model interpretability, and data privacy are also discussed to ensure the responsible integration of LLMs into clinical practice. Overall, our findings offer valuable insights into the strengths and limitations of LLM-based diagnostic systems and provide a roadmap for future enhancements in AI-driven healthcare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
