With the rapid growth of artificial intelligence (AI) in healthcare, there
has been a significant increase in the generation and storage of sensitive
medical data. This abundance of data, in turn, has propelled the advancement of
medical AI technologies. However, concerns about unauthorized data
exploitation, such as training commercial AI models, often deter researchers
from making their invaluable datasets publicly available. In response to the
need to protect this hard-to-collect data while still encouraging medical
institutions to share it, one promising solution is to introduce imperceptible
noise into the data. This method aims to safeguard the data against
unauthorized training by inducing degradation in model generalization. Although
existing methods have shown commendable data protection capabilities in general
domains, they tend to fall short when applied to biomedical data, mainly due to
their failure to account for the sparse nature of medical images. To address
this problem, we propose the Sparsity-Aware Local Masking (SALM) method, a
novel approach that selectively perturbs significant pixel regions rather than
the entire image as previous strategies have done. This simple-yet-effective
approach significantly reduces the perturbation search space by concentrating
on local regions, thereby improving both the efficiency and effectiveness of
data protection for biomedical datasets characterized by sparse features.
Besides, we have demonstrated that SALM maintains the essential characteristics
of the data, ensuring its clinical utility remains uncompromised. Our extensive
experiments across various datasets and model architectures demonstrate that
SALM effectively prevents unauthorized training of deep-learning models and
outperforms previous state-of-the-art data protection methods.

通过在数据中引入不可察觉的噪声来保护医学数据的隐私，在医学影像数据中应用新方法，选择性地扰乱重要像素区域，提高数据保护的效率和效果，防止未经授权对深度学习模型进行训练。

医学不可学习的示例：通过稀疏感知的本地屏蔽保护医学数据免受未授权的训练

Medical Unlearnable Examples: Securing Medical Data from Unauthorized  Traning via Sparsity-Aware Local Masking

In this study, we introduce PharmacyGPT, a novel framework to assess the
capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in
emulating the role of clinical pharmacists. Our methodology encompasses the
utilization of LLMs to generate comprehensible patient clusters, formulate
medication plans, and forecast patient outcomes. We conduct our investigation
using real data acquired from the intensive care unit (ICU) at the University
of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable
insights into the potential applications and limitations of LLMs in the field
of clinical pharmacy, with implications for both patient care and the
development of future AI-driven healthcare solutions. By evaluating the
performance of PharmacyGPT, we aim to contribute to the ongoing discourse
surrounding the integration of artificial intelligence in healthcare settings,
ultimately promoting the responsible and efficacious use of such technologies.

通过评估 PharmacyGPT 在临床药学领域中的应用潜力和限制，并为未来的人工智能驱动医疗解决方案的发展提供有价值的见解，我们旨在促进人工智能在医疗环境中负责任且有效的使用。