This study demonstrates the first in-hospital adaptation of a cloud-based AI, similar to ChatGPT, into a secure model for analyzing radiology reports, prioritizing patient data privacy. By employing a unique sentence-level knowledge distillation method through contrastive learning, we achieve over 95% accuracy in detecting anomalies. The model also accurately flags uncertainties in its predictions, enhancing its reliability and interpretability for physicians with certainty indicators. These advancements represent significant progress in developing secure and efficient AI tools for healthcare, suggesting a promising future for in-hospital AI applications with minimal supervision.

翻译：本研究首次展示了将云端AI（类似于ChatGPT）适配到安全模型中，用于分析放射学报告，并优先保障患者数据隐私的医院内部应用。通过采用一种基于对比学习的独特句子级知识蒸馏方法，我们在异常检测中实现了超过95%的准确率。该模型还能准确标记其预测中的不确定性，借助确定性指标增强其对医生的可靠性和可解释性。这些进展代表了开发安全高效的医疗保健AI工具的重要突破，为在最小监督下实现医院内部AI应用提供了广阔前景。