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.

Current imaging AI systems can be repurposed to address future pandemics, study asserts
Experts say these new findings represent “a significant advancement in the field of radiology."
GPT-4o outperforms radiologists at CT protocoling
Context is the key to accurate and effective large language model protocoling for CT scans, according to new research.
Rad Partners commercializes teleradiology platform pioneered by acquired company vRad
The product is a fully integrated, artificial intelligence-enabled technology tool, providing real-time analytics to interested outside radiology groups.
Radiology experts advocate against purging decade-old imaging data
Researchers with the University of Virginia recently aimed to answer this question, sharing their findings in the Journal of Imaging Informatics in Medicine.
Are LLMs ready for radiology? Research reveals how chatbots' performance has evolved over time
Large language models have shown great potential in medical imaging, but their success has been hindered by their wavering effectiveness. Now, nearly three years after ChatGPT's release, experts are sharing new insights.

Chest radiograph in a 53-year-old woman with confirmed coronavirus disease 2019, or COVID-19, pneumonia. The patient had fever and cough for 5 days. Multifocal patchy opacities (arrows) can be seen in both lungs. Image courtesy of RSNA. What COVID looks like ob CT medical imaging.

Current imaging AI systems can be repurposed to address future pandemics, study asserts

Experts say these new findings represent “a significant advancement in the field of radiology."

ChatGPT large language models radiology health care

GPT-4o outperforms radiologists at CT protocoling

Context is the key to accurate and effective large language model protocoling for CT scans, according to new research.

Rad Partners commercializes teleradiology platform pioneered by acquired company vRad

The product is a fully integrated, artificial intelligence-enabled technology tool, providing real-time analytics to interested outside radiology groups.

data standardization Enlitic enterprise imaging

Radiology experts advocate against purging decade-old imaging data

Researchers with the University of Virginia recently aimed to answer this question, sharing their findings in the Journal of Imaging Informatics in Medicine.

artificial intelligence healthcare industry digest

Are LLMs ready for radiology? Research reveals how chatbots' performance has evolved over time

Large language models have shown great potential in medical imaging, but their success has been hindered by their wavering effectiveness. Now, nearly three years after ChatGPT's release, experts are sharing new insights.

Dana Smetherman, MD, MPH, MBA, FACR, chief executive officer of the American College of Radiology (ACR), explains ACR efforts to help members understand and enable easier integration of artificial intelligence (AI) into practice while also ensuring it can be trusted.

ACR expands programs to help radiology practices integrate and trust AI

CEO Dana Smetherman, MD, MPH, MBA, explains American College of Radiology efforts to help members understand and enable easier integration of artificial intelligence.

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Short-term breast cancer risk prediction improves when AI combines imaging with personal info

Understanding which women have the greatest short-term risk could enable providers to implement targeted screening strategies to ensure malignancies are caught at the earliest possible stage.

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Primary care providers embrace AI-generated imaging results explanations

Stanford Health Care has developed an electronic health record-integrated tool for helping draft comments on radiology, pathology and lab findings.