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. 

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The key to AI integration? Keeping it straightforward, says GE HealthCare CMO

“To drive adoption, it is important that the technology designed to help productivity doesn’t add more work and complexity,” Dr. Mathias Goyen, Chief Medical Officer for Europe, the Middle East and Africa (EMEA) at GE HealthCare, told Health Imaging.

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Cardiologists use video-based AI model to ID coronary artery disease

A team of specialists out of Cedars-Sinai developed the deep learning model using TTEs from nearly 3,000 patients.

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Most radiology residents want more AI training, but few are getting it

The majority of radiology trainees have expressed interest in furthering their knowledge of artificial intelligence applications in the field, yet few are offered the opportunity to do so during the course of their education, according to new survey data.

Automated breast ultrasound (ABUS) allows for reproducible breast imaging without variation based on which sonographer performs the exam. It also can help centers were they are short on qualified breast sonographers.  Breast ultrasound can help identify cancers, or benign cysts, even in women with very dense breast tissue. At the GE Healthcare booth at RSNA.

Commercially available AI systems excel in cancer detection in dense breasts

A multi-modal AI approach can combine information from both ABUS and DM, which could be especially beneficial in resource poor regions where experienced radiologists might not be readily available.

Example of artificial intelligence generated measurements to quantify the size of a lung cancer nodule during a followup CT scan to see if the lesion is regressing with treatment. This type of automation can aid radiologists by doing the tedious, time consuming work. Photo by Dave Fornell

8 trends in radiology technology to watch in 2023

Here is a list of some key trends in radiology technology from our editors based on our coverage of the radiology market.

Bayer acquires AI solutions provider Blackford Analysis

The Edinburgh-based business made the announcement on Jan. 18, noting that the acquisition will build on the company’s goals to “improve the lives of patients and populations by unlocking the adoption and benefits of medical imaging AI.” 

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AI tool predicts lung cancer without radiologists or clinical histories

The deep learning model was trained to predict risk of lung cancer in the one to six years following completion of an LDCT scan, and it does not require clinical information relative to risk factors to do so.

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AI in radiology: How do rad techs feel about its use?

Many radiologic technologists strongly feel that AI technologies should be incorporated into the curriculum for today’s emerging radiographers. 

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