AIMC Topic: Confidentiality

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Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye.

BMC medical ethics
BACKGROUND: Ethical decision-making in child and adolescent psychiatry (CAP) is inherently complex, shaped by developmental vulnerability, evolving autonomy, and competing responsibilities to patients, families, and the legal system. Clinicians often...

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...

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...

Who reviewed this? Toward responsible integration of large language models for peer review of scientific articles in dental medicine.

Swiss dental journal
The introduction and advancement of large language models (LLMs), such as ChatGPT, DeepSeek, and Google Gemini, present both opportunities and challenges for peer review in dental research. In this article, we propose a framework to inform the discou...

A hybrid ECC-AES encryption framework for secure and efficient cloud-based data protection.

Scientific reports
In digital healthcare, ensuring the privacy and security of sensitive mental health data remains a critical challenge. This paper introduces SymECCipher, a novel hybrid encryption framework that integrates Elliptic Curve Cryptography (ECC) for key ex...

Integrating advanced neural network architectures with privacy enhanced encryption for secure and intelligent healthcare analytics.

Scientific reports
Healthcare data protection in our mutually connected era has emerged as an issue of serious concern with private patient information, which has been exposed more often due to data violations and cyber-attacks. Network structures CNN and LSTM as part ...

A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.

Scientific reports
Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. However, sharing sensitive raw medical data with third parties for analysi...

Blockchain framework with IoT device using federated learning for sustainable healthcare systems.

Scientific reports
The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain ...