AI Medical Compendium Topic

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

Delivery of Health Care

Showing 71 to 80 of 1493 articles

Clear Filters

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...

A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.

PloS one
BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart...

Embedded Ethics in Practice: A Toolbox for Integrating the Analysis of Ethical and Social Issues into Healthcare AI Research.

Science and engineering ethics
Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically s...

Other possible perspectives for solving the negative outcome penalty paradox in the application of artificial intelligence in clinical diagnostics.

Journal of medical ethics
Artificial intelligence (AI), represented by machine learning, artificial neural networks and deep learning, is impacting all areas of medicine, including translational research (from bench to bedside to health policy), clinical medicine (including d...

Machine learning in healthcare citizen science: A scoping review.

International journal of medical informatics
OBJECTIVES: This scoping review aims to clarify the definition and trajectory of citizen-led scientific research (so-called citizen science) within the healthcare domain, examine the degree of integration of machine learning (ML) and the participatio...

Knowledge is not all you need for comfort in use of AI in healthcare.

Public health
OBJECTIVES: The adoption of artificial intelligence (AI) in healthcare is rapidly expanding, transforming areas such as diagnostics, drug discovery, and patient monitoring. Despite these advances, public perceptions of AI in healthcare, particularly ...

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 ...

Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications.

International journal of medical informatics
BACKGROUND: The development of Artificial Intelligence in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality data due to issues in data co...