Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

Physician shortage in radiology, other specialties could surpass 35,000 by 2034, but AI also a factor

Artificial intelligence could improve rads' productivity but also decrease demand for their services, according to a new Association of American Medical Colleges report. 

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Combining AI with cardiac imaging helps predict heart attacks, cardiovascular deaths

The findings were presented virtually during the Society of Nuclear Medicine and Molecular Imaging's 2021 Annual Meeting.

Will Nurse Grace cheer patients up or scare them away?

Empathetic, affable, visually unthreatening and coolly competent in several healthcare tasks, a newly trained nurse named Grace has made a head-turning debut.

Storytelling robots send parents of young children into AI’s ‘uncanny valley’

Many parents would let their children be read to by robots as long as the device didn’t project a little too much lifelikeness.

‘Tic Tac Toe’ MRI technique uncovers sickle cell’s impact on the brain

An estimated 100,000 people in the U.S. live with sickle cell disease, which disproportionately affects African Americans.

New tools, techniques emerge to extend AI’s adaptability in cloud-based drug discovery

Because they learn as they go, machine learning models for drug discovery have to be continuously re-trained for changing conditions in drug production processes.

AI teams with fMRI to advance the state of deep brain stimulation

The system hit 88% accuracy at optimizing stimulation settings, as confirmed by brain-response patterns on neuroimaging as well as visibly observable symptom improvement in patients with Parkinson’s disease.

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New AI model uses readily available healthcare data to predict type 2 diabetes

The team’s algorithm was trained, validated and tested with data from a total of more than 2.1 million patients.