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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Stratifying patients by risk of poor outcomes could reduce overtreatment of lung cancer

Researchers are using radiomics to narrow patient cohorts down to those who are at the greatest risk of poor lung cancer outcomes.

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AI spots pancreatic cancer in its earliest stages

Experts involved in the study suggest their findings could eventually be used to detect pancreatic cancer in its earliest stages when patients are most likely to respond to interventions favorably.

AI aids coma prognostics, potentially averting withdrawal of care

Deep learning has bested experienced neurosurgeons at predicting poor outcomes, including mortality, among patients admitted comatose with severe traumatic brain injuries.

Machine learning model quickly and accurately predicts outcomes for TBI patients

The model combines clinical data with imaging from head CT scans in individuals with severe traumatic brain injuries to quickly predict 6-month outcomes.

NASA holoportation

NASA beams doctor virtually into space

Telemedicine boomed in popularity during the COVID-19 pandemic, but remote healthcare isn’t just for homebound patients––it’s also now available to astronauts.

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AI-based mammo screening protocol reduces radiologist workload by 62%

Researchers reported that the artificial intelligence system was able to interpret more than 114,000 screening mammograms using a reading protocol with high sensitivity and specificity.

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Explainable AI model accurately auto-labels chest X-rays from open access datasets

A model that can achieve accuracy in line with that of radiologists when labeling open-access datasets could be a key factor to overcoming limitations of artificial intelligence implementation, researchers explained in Nature Communications.

Algorithm performs at expert level when distinguishing between benign and malignant ovarian tumors

Experts involved with the study suggested that these findings could be beneficial in the future of ovarian tumor assessment by providing clinical decision making support.

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