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arXiv · 2503.20794

Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?

Veysel Kocaman·Muhammed Santas·Yigit Gul·Mehmet Butgul·David Talby
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Abstract

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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Veysel Kocaman, Muhammed Santas, Yigit Gul, Mehmet Butgul, David Talby. 2025-03-31. Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?. https://arxiv.org/abs/2503.20794

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