Health IT

Healthcare information (HIT) systems are designed to connect all the elements together for patient data, reports, medical imaging, billing, electronic medical record (EMR), hospital information system (HIS), PACS, cardiology information systems (CVIS)enterprise image systemsartificial intelligence (AI) applications, analytics, patient monitors, remote monitoring systems, inventory management, the hospital internet of things (IOT), cloud or onsite archive/storage, and cybersecurity.

An example of commercially available artificial intelligence (AI) automated grading of breast density on mammograms from the vendor Densitas..

VIDEO: Role of AI in breast imaging with radiomics, detection of breast density and lesions

Connie Lehman, MD, chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital, discusses how artificial intelligence (AI) is being implemented in breast imaging.

Psychiatry, neuroradiology researchers find imaging markers of inheritable depression

Children are at heightened risk of major clinical depression when at least one parent has a history of the disorder. New research shows depression markers appearing on structural and functional brain MRI ahead of symptoms in these “familial risk” offspring from infancy through early adulthood.

Total body PET/CT scans may offer benefits for evaluating arthritis

Low-dose scans showed high agreement with joint-by-joint rheumatological evaluations. 

 

Brain imaging scans unlock mysteries about depression and resilience

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

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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.  

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.

Around the web

The ACR hopes these changes, including the addition of diagnostic performance feedback, will help reduce the number of patients with incidental nodules lost to follow-up each year.

And it can do so with almost 100% accuracy as a first reader, according to a new large-scale analysis.

The patient, who was being cared for in the ICU, was not accompanied or monitored by nursing staff during his exam, despite being sedated.