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.

Is AI just what the doctor ordered for the radiologist shortage? Yes and no
AI shines in radiology when tasked with interpreting images of patients whose odds of illness are either very high or very low. In these cases, the technology could help mitigate burnout and supplement staffing.
AI predicts pediatric cancer recurrence with impressive accuracy
Researchers have developed a deep learning model that can spot longitudinal changes in patients' brain imaging that could be indicative of cancer recurrence.
AI helps cardiologists terminate AFib for good with ablation
Advanced AI can provide real-time feedback to cardiologists, helping them deliver high-quality care.
Big-name healthcare orgs tap AI to improve breast imaging workflows
These moves come as healthcare organizations are increasingly seeking ways to integrate artificial intelligence into daily clinical duties.
Congress should relax budget neutrality to pay for imaging AI, industry lobbying group urges
"As the nation’s largest payer of healthcare, Medicare’s policies on coverage and payment for AI technologies are especially critical," AdvaMed says.

Americans wary of face recognition technology in healthcare

Only two-thirds of U.S. healthcare consumers are OK with surgeons using digital facial recognition to avoid medical error by confirming patient identity.

Artificial intelligence matches radiologists at predicting patients’ emphysema severity

Outputs from the assessed AI prototypes were robust across different scanners, though larger patients posed challenges, experts wrote in Academic Radiology.

Why deep learning trained on radiologist-labeled data may be worth added time, costs

This proved true for detecting pneumothorax, as networks developed on hand-labeled images outperformed tools refined via natural language processing.

AI in radiology is just getting started, but these 4 lessons can help practices prepare

Massachusetts General Hospital gathered reports from those on the front lines of the coming AI revolution, sharing their tips for success in JACR.

Physicians’ behaviors are nearly all AI needs to head off faulty drug prescriptions

Contrary to intuitive expectations, many errors in drug ordering are caused or worsened by the intricacies of the EHR.

AI helps uncover, illustrate the inner workings of cells

Researchers have used electron microscopes and machine learning to create detailed, high-resolution 3D images of subcellular structures called organelles.

AI-based mammography triage helps providers dramatically improve interpretation process

California radiologists have seen average turnaround times and flags per exam plummet, with skepticism quickly dissipating, experts wrote in JACR.

AI finds forgotten surgical instruments in patients’ bodies

In hospitals where patients are routinely X-rayed following surgery, AI-equipped CAD software could screen for left-behinds automatically.

Artificial Intelligence

Is AI just what the doctor ordered for the radiologist shortage? Yes and no

AI predicts pediatric cancer recurrence with impressive accuracy

AI helps cardiologists terminate AFib for good with ablation

Big-name healthcare orgs tap AI to improve breast imaging workflows

Congress should relax budget neutrality to pay for imaging AI, industry lobbying group urges