Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis

Kyungsu Kim, Junhyun Park, Saul Langarica, Adham Mahmoud Alkhadrawi, Synho Do
2/14/2024
cs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{kim2024integrating,
  title  = {Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis},
  author = {Kyungsu Kim and Junhyun Park and Saul Langarica and Adham Mahmoud Alkhadrawi and Synho Do},
  year   = {2024},
  eprint = {2402.09358},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.09358v1}
}

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