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. 

USA China artificial intelligence race

China’s AI concerns presented for public consumption

Tech-enabled risks are top of mind for the head of the Chinese Communist Party—and AI is prominent among these.

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AI tool helps identify 'invisible' head injuries on MRIs of college athletes

The tool uses a machine learning technique to identify changes on brain MRIs that would otherwise be overlooked by radiologists due to the subtlety of alterations. 

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FDA clears GE HealthCare’s AI solution for enhancing PET/CT image quality

Precision DL was engineered using a deep neural network, trained with thousands of images, the Chicago-based company said May 30. 

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Ultrasound AI could help standardize trauma care

Trauma patients who present with poor blood flow and suspected abdominal hemorrhage are well served by emergency physicians using AI-augmented FAST imaging.

Industry Watcher’s Digest

Buzzworthy developments of the past few days.

Center for AI Safety extinction statement

Tech thought leaders issue terse warning on humanity’s ‘risk of extinction’ at the hands of AI

Several hundred AI experts, stakeholders and commentators are alerting the world to the technology’s potential for widespread harm. 

artificial intelligence robot evaluates healthcare data

AI organizes heart failure patients into 5 distinct groups, helping cardiologists manage care

Researchers used multiple AI models to evaluate EHR data from more than 322,000 heart failure patients. By identifying these subtypes, the group thinks clinicians could perform better risk assessments and make more informed treatment decisions. 

artificial intelligence in radiology medical imaging interpretation

Interpretive AI for medical imaging: 5 points of skepticism, idealism

Surveying the landscape of interpretive AI in radiology, two researchers note a yawning gap between great expectations set in the recent past and actual clinical implementations as of spring 2023.