AI Medical Compendium Topic:
Delivery of Health Care

Clear Filters Showing 581 to 590 of 1500 articles

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities.

Journal of healthcare engineering
Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud c...

Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response.

Computational and mathematical methods in medicine
In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be proces...

A Federated Mining Approach on Predicting Diabetes-Related Complications: Demonstration Using Real-World Clinical Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chronic diabetes can lead to microvascular complications, including diabetic eye disease, diabetic kidney disease, and diabetic neuropathy. However, the long-term complications often remain undetected at the early stages of diagnosis. Developing a ma...

Using Machine Learning to Support Transfer of Best Practices in Healthcare.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The adoption of best practices has been shown to increase performance in healthcare institutions and is consistently demanded by both patients, payers, and external overseers. Nevertheless, transferring practices between healthcare organizations is a...

MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare.

Computer methods in biomechanics and biomedical engineering
In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart diseas...

The ethical challenges of artificial intelligence-driven digital pathology.

The journal of pathology. Clinical research
Digital pathology - the digitalisation of clinical histopathology services through the scanning and storage of pathology slides - has opened up new possibilities for health care in recent years, particularly in the opportunities it brings for artific...

Effect of risk, expectancy, and trust on clinicians' intent to use an artificial intelligence system -- Blood Utilization Calculator.

Applied ergonomics
A gap exists between the capabilities of artificial intelligence (AI) technologies in healthcare and the extent to which clinicians are willing to adopt these systems. Our study addressed this gap by leveraging 'expectancy-value theory' and 'modified...

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis.

Frontiers in public health
The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about...

SHIFTing artificial intelligence to be responsible in healthcare: A systematic review.

Social science & medicine (1982)
A variety of ethical concerns about artificial intelligence (AI) implementation in healthcare have emerged as AI becomes increasingly applicable and technologically advanced. The last decade has witnessed significant endeavors in striking a balance b...

Machine learning and artificial intelligence in research and healthcare.

Injury
Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the proc...