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Robust privacy amidst innovation with large language models through a critical assessment of the risks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language mo...

Digital transformation in healthcare management: from Artificial Intelligence to blockchain.

Wiadomosci lekarskie (Warsaw, Poland : 1960)
The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes, and data security. With the increasing complexity of healthcare systems, the integration of cuttin...

Attribute encryption access control method of high dimensional medical data based on fuzzy algorithm.

PloS one
The current approach to data access control predominantly utilizes blockchain technology. However, when dealing with high-dimensional medical data, the inherent transparency of blockchain conflicts with the necessity of protecting patient privacy. Co...

[Legal Risk Assessment and Prevention in Artificial Intelligence-Assisted Health Care].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
With the wide application of new technologies such as large language models and generative artificial intelligence (AI) in the health care sector, artificial intelligence-assisted health care is confronted with new forms of legal risks. The algorithm...

Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: a scoping review.

BMC medical ethics
BACKGROUND: Humanitarian organizations are rapidly expanding their use of data in the pursuit of operational gains in effectiveness and efficiency. Ethical risks, particularly from artificial intelligence (AI) data processing, are increasingly recogn...

Bridging the gaps: Overcoming challenges of implementing AI in healthcare.

Med (New York, N.Y.)
Artificial intelligence (AI) in healthcare promises transformative advancements, from enhancing diagnostics to optimizing personalized treatments. Realizing its full potential, however, requires addressing key challenges, including explainability, bi...

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Sensors (Basel, Switzerland)
Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedde...

Data Governance in Healthcare AI: Navigating the EU AI Act's Requirements.

Studies in health technology and informatics
The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize patient care, diagnostics, and treatment planning. However, this integration also introduces significant challenges related to data governance, privacy...

Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI.

Scientific reports
Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intel...

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Die Ophthalmologie
Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial use of artificial intelligence (AI). The analysis of images and data by trained algorithms has the potential to facilitate making the diagnosis and pat...