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. 

Thumbnail

IBM sale of Watson Health reveals healthcare’s AI conflict

The reported exploration to sell Watson Health, the AI-driven health business of IBM, underscores deep challenges with technology in solving healthcare problems, according to the Wall Street Journal.

 

colon colorectal cancer CTC

Radiomics tool spots colon polyps before they evolve into cancer

Colorectal cancer remains one of the top three causes of cancer-related death among men and women in industrialized regions, researchers explained in Radiology.

Thumbnail

East Coast university scores $1.2M grant for cutting-edge biological imaging projects

The New York-based institution says the funds will help to develop a new light-sheet imaging device that will power a handful of cellular-level experiments.

Politicians who break party lines elicit stronger brain responses, new imaging study reveals

Neuroimaging showed increased activity in two areas involved in cognitive function, researchers explained Monday.

Thumbnail

American College of Radiology Data Science Institute releases 6 new AI use cases

ACR said the updates pertain to neuroradiology and cover clinical scenarios including white matter lesion tracking in multiple sclerosis patients. 

chest pain lung pulmonary embolism

AI spots dozens of missed incidental pulmonary embolism diagnoses at one hospital

The investigation was retrospective, but Duke scientists believe their algorithm could potentially aid radiologists in spotting near-misses in their work.

Call made for more rigorous evaluation of AI aimed at guiding providers, patients

Only two of 34 representative studies evaluating the use of AI for real-world shared clinical decisionmaking from 2014 to 2020 included external validation of the models up for consideration.

Ethics researchers warn of healthcare AI’s potential for widening gaps between haves and have-nots

Healthcare AI is advancing too quickly for its users to fully comprehend the implications of its design, development and applications, according to bioethics specialists who scanned the literature.