Enterprise Imaging

Enterprise imaging brings together all imaging exams, patient data and reports from across a healthcare system into one location to aid efficiency and economy of scale for data storage. This enables immediate access to images and reports any clinical user of the electronic medical record (EMR) across a healthcare system, regardless of location. Enterprise imaging (EI) systems replace the former system of using a variety of disparate, siloed picture archiving and communication systems (PACS), radiology information systems (RIS), and a variety of separate, dedicated workstations and logins to view or post-process different imaging modalities. Often these siloed systems cannot interoperate and cannot easily be connected. Web-based EI systems are becoming the standard across most healthcare systems to incorporate not only radiology, but also cardiology (CVIS), pathology and dozens of other departments to centralize all patient data into one cloud-based data storage and data management system.

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Video of Tim Kearns explaining the new Konica Minolta Exa Enterprise imaging system released at RSNA 2024.

Enterprise imaging expands in smaller and midsized hospitals

Smaller health systems are increasingly moving into this realm. Tim Kearns, director of marketing and healthcare IT, Konica Minolta Imaging USA, explains the implications.

 

chatgpt artificial intelligence healthcare

How do radiologists feel about utilizing GPT-4 in practice?

In recent years, there has been much talk of the potential for large language models to improve radiology workflows.

AI-based 3D CTA reconstruction solution scores FDA clearance

Using the new solution, CTA recons are completed in a matter of minutes, not hours.

Nina Kottler, MD, Radiology Partners, offers overview of the U.S. AI regulatory landscape as government and radiologists work on ways to ensure artificial intelligence is not bias and works properly.

Overview of the regulatory landscape of AI in radiology

Nina Kottler, MD, associate CMO for clinical AI at Radiology Partners, explains the movement toward greater regulation of artificial intelligence and the need to test for bias. 

Medical imaging trends to watch in 2025

The healthcare market analysis firm Signify Research released a list of predictions in radiology its analysts expect to see in 2025. 

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GPT-4 helps ensure recommendations for additional imaging aren't overlooked in reports

Recommendations for additional imaging are routinely included in radiology reports but are sometimes overlooked or not communicated in a timely manner. Experts believe large language models can help address these lapses in care. 

GPT-4 can proofread radiology reports for a penny apiece

Researchers estimate that it could cost less than $0.01 per report to use the large language model as a radiology report proofreader. 

DL model identifies and segments lung tumors on CT scans.

Deep learning model halves lung tumor segmentation times

In a new clinical study, the model was able to maintain its performance on scans completed on different types of CT equipment across multiple medical centers.  

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