2024-07-24-kim24a.md

title	abstract	year	volume	publisher	series	software	layout	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis	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. Despite limitations in data privacy during the training phase, such as requiring de-identification or IRB permission, our study is significant in addressing this issue in the inference phase (once the local model is trained), without the need for human annotation throughout the entire process. These advancements represent a new direction for developing secure and efficient AI tools for healthcare with minimal supervision, paving the way for a promising future of in-hospital AI applications.	2024	248	PMLR	Proceedings of Machine Learning Research	https://github.com/MGH-LMIC/LLM_normal	inproceedings	2640-3498	kim24a	0	Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis	72	87	72-87	72	false	Kim, Kyungsu and Park, Junhyun and Langarica, Saul and Mahmoud Alkhadrawi, Adham and Do, Synho	given family Kyungsu Kim given family Junhyun Park given family Saul Langarica given family Adham Mahmoud Alkhadrawi given family Synho Do	2024-07-24		Proceedings of the fifth Conference on Health, Inference, and Learning	inproceedings	date-parts 2024 7 24	https://raw.githubusercontent.com/mlresearch/v248/main/assets/kim24a/kim24a.pdf