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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Example of a cancer that is difficult to see in dense breast tissue, but can be seen easier using 3D mammography digital breast tomosynthesis (DBT) breast imaging because the radiologist can go through the breast layer by layer if tissue..

VIDEO: The rapid adoption of 3D mammography and use of AI to address dense breasts

Stamatia Destounis, MD, a radiologist and managing partner at Elizabeth Wende Breast Care in Rochester, New York, chair of the American College of Radiology (ACR) Breast Commission, explains the rapid adoption of 3D mammogram digital breast tomosynthesis (DBT) technology.
 

Society of Breast Imaging (SBI) President John Lewin, MD, explains some of new initiatives and technology in mammography to increase earlier breast cancer detection. #SBI #breastimaging #mammography

VIDEO: SBI president outlines trends in breast imaging

Society of Breast Imaging President John Lewin, MD, explains some of the new initiatives and technology in mammography that are designed to increase early breast cancer detection.

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VIDEO: KLAS shares trends in enterprise imaging and AI

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains some of technology trends KLAS researchers have found in enterprise imaging system and radiology artificial intelligence (AI).

Charles E. Kahn, Jr., MD, MS, Editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He has been heavily involved in radiology informatics and has seen up close the evolution of radiology toward deeper integration with AI. #RSNA

VIDEO: Use cases and implementation strategies for radiology artificial intelligence

Charles Kahn, Jr., MD, editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, explains the work involved integrating AI in radiology systems and the role of AI in augmenting patient care.
 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He discusses the need to validate artificial intelligence (AI) algorithms with your own patient population to determine if it is accurate for a specific institutions patients. He also explains how bias can be inadvertently added into a algorithm, and how the AI may take learning shortcuts. #AI

VIDEO: Assessing radiology AI and understanding programatic bias 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA  journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, discusses the need to validate AI algorithms with your own patient population data.  

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Prostate cancer detection boosted with computer assistance

The addition of computer-aided diagnostic generated MRI series could help radiologists identify clinically significant prostate cancer more frequently. 

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AI software's pediatric fracture detection in line with that of radiologists

An artificial intelligence system that is currently commercially available for use in adults could also have applications in a pediatric population, according to a new study in Pediatric Radiology.

Radiomics-based models can detect pancreatic cancer well before clinical diagnosis

Recently a radiomics-based machine learning model proved highly accurate at predicting which patients would develop pancreatic cancer three to 36 months after abdominal CT imaging.

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