AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Information Dissemination

Showing 1 to 10 of 154 articles

Clear Filters

Infodemic Versus Viral Information Spread: Key Differences and Open Challenges.

JMIR infodemiology
As we move beyond the COVID-19 pandemic, the risk of future infodemics remains significant, driven by emerging health crises and the increasing influence of artificial intelligence in the information ecosystem. During periods of apparent stability, p...

Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.

Computer methods and programs in biomedicine
BACKGROUND: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promisin...

Target informed client recruitment for efficient federated learning in healthcare.

BMC medical informatics and decision making
BACKGROUND: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available ...

The critical need for an open medical imaging database in Japan: implications for global health and AI development.

Japanese journal of radiology
Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. While Japan maintains extensive repositories, access restrictions limit their research utility, cont...

Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health information sharing. This study a...

Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.

BMC medical informatics and decision making
Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data colle...

Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand.

Journal of medical Internet research
BACKGROUND: Health care systems in low- and middle-income countries (LMICs) can greatly benefit from artificial intelligence (AI) interventions in various use cases such as diagnostics, treatment, and public health monitoring but face significant cha...

Ethical boundaries and data-sharing practices in AI-enhanced nursing: An Arab perspective.

International nursing review
AIM: This study explored the ethical boundaries and data-sharing practices in artificial intelligence (AI)-enhanced nursing from the perspective of Arab nurses.

Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity.

Scientific reports
In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, lever...

Secure healthcare data sharing and attack detection framework using radial basis neural network.

Scientific reports
Secure medical data sharing and access control play a prominent role. However, it is still unclear how to provide a security architecture that can guarantee the privacy and safety of sensitive medical data. Existing methods are application-specific a...