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.

GE HealthCare touts international expansion of its partnership with RadNet
The companies say they’re entering the next phase of collaboration, offering GEHC mammography systems paired with AI offerings from DeepHealth.
Healthcare AI often arrives with great expectations only to disappoint end-users as well as execs. Why is that?
By and large, U.S. hospitals and health systems do not have an AI adoption problem. But many—if not most—have an execution problem: They struggle to turn a set of installed AI tools into a measurable operational value.
Interventional radiologist launches AI-powered, IR-specific decision support platform
Developed by a board-certified diagnostic and interventional radiologist, the platform was trained using subspecialty data that general AI applications typically lack.
AI scribes add at least $167 per month to clinician income: 5-site study
In the three or so years since it burst into healthcare, ambient AI scribe technology has run away with the win in the market-uptake race. What has been the return on investment in all those software packages?
Healthcare cybersecurity has never been so complicated—or so plannable
AI technologies have changed daily life for provider organizations and industry partners alike. Arguably the biggest single difficulty to emerge from the complex transformation is the risk of cybersecurity breaches.

GE HealthCare touts international expansion of its partnership with RadNet

The companies say they’re entering the next phase of collaboration, offering GEHC mammography systems paired with AI offerings from DeepHealth.

Healthcare AI often arrives with great expectations only to disappoint end-users as well as execs. Why is that?

By and large, U.S. hospitals and health systems do not have an AI adoption problem. But many—if not most—have an execution problem: They struggle to turn a set of installed AI tools into a measurable operational value.

artificial intelligence AI deep learning ChatGPT OpenAI Microsoft Google

Interventional radiologist launches AI-powered, IR-specific decision support platform

Developed by a board-certified diagnostic and interventional radiologist, the platform was trained using subspecialty data that general AI applications typically lack.

AI scribes add at least $167 per month to clinician income: 5-site study

In the three or so years since it burst into healthcare, ambient AI scribe technology has run away with the win in the market-uptake race. What has been the return on investment in all those software packages?

Healthcare cybersecurity has never been so complicated—or so plannable

AI technologies have changed daily life for provider organizations and industry partners alike. Arguably the biggest single difficulty to emerge from the complex transformation is the risk of cybersecurity breaches.

overweight patient seeking health advice

Novo Nordisk partners with OpenAI to speed development of new weight loss drugs

The maker of popular GLP-1 agonists Wegovy and Ozempic said technology from OpenAI, the company behind ChatGPT, will allow it to sift through and make sense of massive datasets to identify potential new uses for its diabetes and obesity treatments.

Breast cancer AI ribbon pink artificial intelligence

New guidelines recommend AI-based breast cancer risk assessments

NCCN suggests that 5-year future breast cancer risk assessments based on routine mammograms should be integrated into standard practices.

Another win for opportunistic screening: AI turns head CT scans into heart assessments

AI can help care teams get additional value out of routine head CT images.