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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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.

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.  

artificial intelligence healthcare industry digest

Prompts matter: How input elements affect large language model performance

Just like radiologists, the performance of large language models improves when given proper context. 

Video of Steve Rankin, chief strategy officer for Enlitic, explaining how AI can help standardize labeling of medical images.

AI can help radiology standardize image exam data labeling

To fully leverage today's radiology IT systems, standardization is a necessity. Steve Rankin, chief strategy officer for Enlitic, explains how artificial intelligence can help.

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