Imaging Informatics

Imaging informatics (also known as radiology informatics, a component of wider medical or healthcare informatics) includes systems to transfer images and radiology data between radiologists, referring physicians, patients and the entire enterprise. This includes picture archiving and communication systems (PACS), wider enterprise image systems, radiology information. systems (RIS), connections to share data with the electronic medical record (EMR), and software to enable advanced visualization, reporting, artificial intelligence (AI) applications, analytics, exam ordering, clinical decision support, dictation, and remote image sharing and viewing systems.

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Brain imaging scans unlock mysteries about depression and resilience

The new findings may contain important implications for neuromodulation therapies to treat depression symptoms.

October 17, 2022
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).

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).

October 13, 2022
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.
 

October 12, 2022
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.  

October 11, 2022
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Google Cloud intros ambitious branch dedicated to medical imaging

A Big Four tech company has launched a platform it hopes will accelerate data interoperability and AI adoption in, specifically, medical imaging.

October 5, 2022
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.

September 29, 2022
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.

September 28, 2022
Clinician and physician burnout is fueling the large numbers of resignations in healthcare, which are fueled by a handful of factors, including adequate staffing and being bogged down in non-clinical work, especially with inefficient EMRs.

VIDEO: AI can help prevent clinician burnout

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, discusses how artificial intelligence (AI) can help combat clinician burnout.

September 13, 2022
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Around the web

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Medicare drops AUC requirement for advanced imaging, ASNC celebrates

"This was an unneeded burden, which was solely adding to the administrative hassles of medicine," said American Society of Nuclear Cardiology President Larry Phillips.

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Cardiology, radiology groups urge insurance provider to rethink imaging policy

SCAI and four other major healthcare organizations signed a joint letter in support of intravascular ultrasound. 

Cardiovascular Business
Radiology AI company gains FDA clearance for new CT offerings focused on PE, stroke

The newly approved AI models are designed to improve the detection of pulmonary embolisms and strokes in patients who undergo CT scans.

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