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.

Healthcare AI today: Bipartisan AI policy guidance, AI claims denials, LLM brain rot, more
Federal lawmakers have a fresh resource to keep them up on healthcare AI that falls outside of formal FDA oversight. It comes in the form of a new report from the nonprofit Bipartisan Policy Center.
Common features of breast cancers missed by AI
Imaging experts advocate for “meticulous assessments” of cases that include luminal cancer, dense breasts, and lesions located outside the mammary zone, among other factors.
Why do AI-savvy consumers harbor skepticism toward medical AI?
Patients who frequently use AI tend to readily trust AI-assisted diagnoses made by their physicians. Counterintuitively, however, those who would rank themselves among the very best-informed about AI tend to mistrust such diagnoses.
AAA detection gets boost from commercially available AI algorithm
The opportunistic screening helps identify patients in need of monitoring and/or intervention without disrupting workflows.
Healthcare AI today: Senate arguments, aging infrastructure, ‘elastic’ jobs, more
States’ rights or federal power? Senate Republicans are of two minds on AI regulation in the One Big Beautiful Bill.

AI technology identifies rare genetic diseases from facial photos

A new artificial intelligence (AI) technology that can identify rare genetic diseases through analyzing an image of a patient's face could help cut diagnosis times for rare diseases and provide more personalized care, according to a new study published online Jan. 7 in Nature Medicine.

Appreciation of machine learning on the rise among imaging professionals

How significant is the hype surrounding artificial intelligence and machine learning in radiology? According to new market research from Reaction Data, 77 percent of imaging professionals said they think machine learning is important when asked about it in 2018, up from 65 percent in 2017.

How virtual reality is taking surgical training to the next level

At Stanford University Medical School in California, virtual reality is helping to make surgical training and planning more efficient and patient-centered all while reframing education for medical students, according to an article published online Jan. 9 by Fortune.

Research may offer new method to detect GBCA on MRI

Notably lower T1 relaxation times, or how brain signals can weaken over time, may point to the presence of gadolinium in the brains of patients who’ve had more than one contrast-enhanced MRI exam, according to research published online Jan. 1 in Radiology.

AI trained with less than 1,000 CT scans may solve 'black box' challenge

Using less than 1,000 imaging cases, researchers from Massachusetts General Hospital (MGH) in Boston were able to train an artificial intelligence (AI) algorithm to detect intracranial hemorrhage (ICH) and classify its five subtypes on unenhanced head CT scans, according to research published in the journal Nature Biomedical Engineering.

Las Vegas trial takes new approach for studying Alzheimer’s, Parkinson’s

A one-of-a-kind trial is underway testing the GE180 radioactive tracer in humans to potentially better understand the root causes of Alzheimer’s and Parkinson’s disease, the Las Vegas Review-Journal reported.

New brain imaging study challenges conventional treatment of schizophrenia

New research published in The American Journal of Psychiatry suggests the brain function of individuals with schizophrenia is more nuanced than previously thought.

App uses AI to forecast hypoglycemic events in diabetics

A new feature for a personal diabetes management application leverages AI to predict the likelihood of a user experiencing a hypoglycemic event in the next one to four hours.

Artificial Intelligence

Healthcare AI today: Bipartisan AI policy guidance, AI claims denials, LLM brain rot, more

Common features of breast cancers missed by AI

Why do AI-savvy consumers harbor skepticism toward medical AI?

AAA detection gets boost from commercially available AI algorithm

Healthcare AI today: Senate arguments, aging infrastructure, ‘elastic’ jobs, more