AIMC Topic: Privacy
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Robust and Privacy-Preserving Decentralized Deep Federated Learning Training: Focusing on Digital Healthcare Applications.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machine learning, gaining widespread attention due to its ability to update parameters without collecting raw data from users, especially in digital health...
Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.
Journal of medical Internet research
Aug 1, 2024
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 ...
Subgraph-level federated graph neural network for privacy-preserving recommendation with meta-learning.
Neural networks : the official journal of the International Neural Network Society
Jul 25, 2024
Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework al...
Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth.
Annual review of biomedical data science
Jul 24, 2024
Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data s...
Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption.
PloS one
Jul 22, 2024
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
Jul 12, 2024
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...
Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jul 3, 2024
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...
Privacy-Preserving Cameras for Fall Detection: Data Acquisition for Artificial Intelligence.
Computers, informatics, nursing : CIN
Jul 1, 2024
Artificial intelligence and its implications for data privacy.
Current opinion in psychology
Jun 22, 2024
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...
Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.
Computer methods and programs in biomedicine
Jun 17, 2024
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...
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