AIMC Topic: Privacy

Clear Filters Showing 1 to 10 of 292 articles

Secure federated transfer learning with enhanced secure multiparty computation for privacy preserving smart EHR systems.

Scientific reports
Federated Learning and Artificial Intelligence (AI) are two most intriguing and leading technologies in the intelligent healthcare business. Data must be collected, stored and analyzed from various companies. Patient data processing, particularly in ...

MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems.

Scientific reports
Recent advances in artificial intelligence have greatly increased the accuracy of computer-assisted diagnosis for serious conditions including brain tumours. However, concerns about data privacy, class imbalance, and the diversity of medical datasets...

Federated nnU-Net for privacy-preserving medical image segmentation.

Scientific reports
The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a central...

A federated incremental blockchain framework with privacy preserving XAI optimization for securing healthcare data.

Scientific reports
Federated learning (FL) has become more popular in the area of machine learning for protecting data privacy, its unique distributed data processing characteristics have garnered widespread attention. However, the implementation of FL faces many chall...

Operationalizing AI ethics in medicine-a co-creation workshop study.

BMC medical ethics
BACKGROUND: A majority of AI ethics frameworks focus on high-level principles but lack actionable guidance. Effectively implementing AI in projects requires the operationalization of AI ethics, translating principles into requirements. This paper pro...

Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model.

Scientific reports
Diabetic retinopathy (DR) is a major problemfor the diabetes patients that makes a serious threat to vision and causes the irreversible blindness if not diagnosed and treated early. Conventional deep learning-based approaches designed for DR detectio...

Adaptive resource aware and privacy preserving federated edge learning framework for real time internet of medical things applications.

Scientific reports
The Internet of Medical Things requires frameworks that ensure secure processing, computational efficiency, and scalability for continuous healthcare data streams. Existing solutions remain limited in their ability to support real-time anomaly detect...

Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges.

BMC medical ethics
BACKGROUND: Artificial intelligence (AI) offers significant potential to drive advancements in healthcare; however, the development and implementation of AI models present complex ethical, legal, social, and technical challenges, as data practices of...

Ethics of nursing in the digital age: perceptions and challenges among Korean nursing students.

BMC medical ethics
BACKGROUND: The advancement of digital technologies has brought transformative changes across the healthcare sector, and nursing is no exception. However, existing research has largely overlooked the ethical challenges nursing students face in real-w...

Privacy preserving skin cancer diagnosis through federated deep learning and explainable AI.

Scientific reports
The classification of human skin disorders, particularly benign and malignant skin cancer, is thoroughly examined in this study with a focus on protecting data privacy. Traditional visual diagnosis of skin disorders is often subjective and complicate...