四款API对比：本地部署的John Snow Labs最准最便宜，达监管级标准。
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?
- 采用本地部署模型，避免云端按令牌计费，成本更低。
- 在48份医学文档上，其隐私信息识别F1值达96%，高于人类专家。
- 唯一满足监管要求且不按使用量收费，适合大规模医疗数据处理。
我们评估了四种领先的非结构化医疗文本脱敏解决方案——Azure Health Data Services、AWS Comprehend Medical、OpenAI GPT-4o 和 John Snow Labs——在由医学专家标注的48份临床文档基准数据集上的表现。实体级与词元级分析显示，John Snow Labs的医学语言模型方案在保护性健康信息（PHI）检测中取得最高准确率，F1得分为96%，优于Azure（91%）、AWS（83%）和GPT-4o（79%）。该方案不仅是唯一达到监管级准确率（超过人类专家水平）的系统，且成本最低：相比Azure和GPT-4o降低超80%，且不按令牌计费。其固定成本的本地部署模式避免了云服务随请求量增长的费用攀升，具备可扩展性和经济性。
原文摘要 · Abstract (English)
We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and John Snow Labs - on a ground truth dataset of 48 clinical documents annotated by medical experts. The analysis, conducted at both entity-level and token-level, suggests that John Snow Labs' Medical Language Models solution achieves the highest accuracy, with a 96% F1-score in protected health information (PHI) detection, outperforming Azure (91%), AWS (83%), and GPT-4o (79%). John Snow Labs is not only the only solution which achieves regulatory-grade accuracy (surpassing that of human experts) but is also the most cost-effective solution: It is over 80% cheaper compared to Azure and GPT-4o, and is the only solution not priced by token. Its fixed-cost local deployment model avoids the escalating per-request fees of cloud-based services, making it a scalable and economical choice.
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