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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Monique Rasband from KLAS Research shares trends in PACS and radiology informatics.

VIDEO: 6 key trends in PACS and radiology informatics observed by KLAS

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, shares some of technology trends observed in radiology PACS and and imaging informatics since 2019.

Validation and testing of all artificial intelligence (AI) algorithms is needed to eliminate any biases in the data used to train the AI, according to HIMSS.

VIDEO: Understanding biases in healthcare AI

Validation and testing of all algorithms is needed to eliminate any biases in the data used to train the AI, according to Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America.

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Majority of radiology residents support implementation of AI-based curriculum

Residents who were given access to the AI-based decision support system reported feeling that the tool was useful in multiple clinical scenarios, and its use was overwhelmingly supported by those who provided feedback. 

Ultrafast MRI protocol reduces scan time by 10 minutes for cervical imaging

Experts involved in the study suggested that the protocol could open doors leading to greater utilization of MRI in the future. 

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Might AI automation improve peer review?

With the software’s help, the ratio of CTs requiring radiologist review to missed findings identified was 10:1, experts shared, adding that without the help of AI that ratio would be at least 66:1. 

A study published this week in the Journal of the American College of Cardiology (JACC): Cardiovascular Imaging shows artificial intelligence (AI) algorithms can more rapidly and objectively determine calcium scores in computed tomographic (CT) and positron emission tomographic (PET) images than physicians.[1] The AI also performed well when the images were obtained from very-low-radiation CT attenuation scans. https://doi.org/10.1016/j.jcmg.2022.06.006

Artificial intelligence can objectively determine cardiac calcium scores faster than doctors

A new study shows artificial intelligence (AI) algorithms can more rapidly and objectively determine calcium scores in CT and PET/CT images than physicians.

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Deep learning reconstruction levels playing field between 1.5T and 3T MRI exams

Denoising using deep learning techniques can boost the performance of 1.5T MR brain imaging, resulting in quality comparable or superior to 3T imaging. 

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AI identifies pancreatic cancer frequently missed on CT

Specifically, the computer-aided detection (CAD) tool is capable of identifying lesions that are less than 2 cm.

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