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Privacy

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Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption.

PloS one
The widespread adoption of cloud computing necessitates privacy-preserving techniques that allow information to be processed without disclosure. This paper proposes a method to increase the accuracy and performance of privacy-preserving Convolutional...

Navigating New Legal Guardrails and Emerging AI Challenges.

The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics
Here, we analyze the public health implications of recent legal developments - including privacy legislation, intergovernmental data exchange, and artificial intelligence governance - with a view toward the future of public health informatics and the...

Artificial intelligence and its implications for data privacy.

Current opinion in psychology
Contemporary, multidisciplinary research sheds light on data privacy implications of artificial intelligence (AI). This review adopts an AI ecosystem perspective and proposes a process-outcome continuum to classify AI technologies; this perspective h...

End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.

BMC medical informatics and decision making
Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the model...

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In h...

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without for...

Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

Journal of medical Internet research
This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of ...

Towards regulatory generative AI in ophthalmology healthcare: a security and privacy perspective.

The British journal of ophthalmology
As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI ...

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

IEEE journal of biomedical and health informatics
In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac abnormalities from electrocardiogram (ECG) data. This approach allows medical institutions to collabor...