AI Medical Compendium Topic:
Big Data

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Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

Journal of the American College of Radiology : JACR
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart f...

A Shared Vision for Machine Learning in Neuroscience.

The Journal of neuroscience : the official journal of the Society for Neuroscience
With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data...

Machine learning in cardiovascular medicine: are we there yet?

Heart (British Cardiac Society)
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing sev...

Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, "The Internet of Things" and Next-Generation Technology Policy.

Omics : a journal of integrative biology
Driverless cars with artificial intelligence (AI) and automated supermarkets run by collaborative robots (cobots) working without human supervision have sparked off new debates: what will be the impacts of extreme automation, turbocharged by the Inte...

Big Data in Public Health: Terminology, Machine Learning, and Privacy.

Annual review of public health
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores se...

Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. ...

Big data analytics and machine learning in hematology: Transformative insights, applications and challenges.

Medicine
The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profi...

Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage th...

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.

Briefings in functional genomics
Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digit...