Informatics

The goal of health informatics systems is to enable smooth transfer of data and cybersecurity across the healthcare enterprise. This includes patient information, images, subspecialty reporting systems, lab results, scheduling, revenue management, hospital inventory, and many other health IT systems. These systems include the electronic medical record (EMR) admission discharge and transfer (ADT) system, hospital information system (HIS), radiology picture archiving and communication systems (PACS), cardiovascular information systems (CVIS), archive solutions including cloud storage and vendor neutral archives (VNA), and other medical informatics systems.

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Brain imaging artificial intelligence is a primary area of concentration for AI because oif the critical nature of fast detection and treatment for patients. This is an example of the AI applications displayed by third-party advanced visualization vendor TeraRecon at RSNA 2022.

What is the ROI on AI adoption in radiology?

Radiology makes up the vast majority of FDA-cleared AI algorithms, but with minimal or no reimbursement, hospital administrators may ask whether AI’s value justifies its expense.

radiology reporting EHR health record CDS AUC

Nuance announces first fully AI-automated clinical documentation tool in healthcare

DAX Express utilizes the advanced reasoning and natural-language capabilities of OpenAI’s GPT-4 to generate draft reports "in seconds," the company said Monday. 

Rankings of radiology IT solutions by end-users in the 2023 Best in KLAS program

End-users of various radiology IT systems offer their assessment of the software in the annual KLAS Research 2023 Best in KLAS report.

The integration of artificial intelligence (AI) into radiology PACS and enterprise imaging systems has become a big topic of discussion with IT vendors over the past couple years. This has become a bigger question from hospitals and radiology groups as there are now about 400 radiology related AI algorithms that have U.S. Food and Drug Administration (FDA) clearance. Amy Thompson, a senior analyst at Signify Research, is monitoring AI trends in radiology and discusses trends.

Trends in the adoption and integration of AI into radiology workflows

Amy Thompson, a senior analyst at Signify Research, explains why AI adoption has been slow in radiology, common barriers and trends in the market.

Why is cloud computing is being adopted in radiology? Amy Thompson, a senior analyst at Signify Research, explains what she is seeing in radiology PACS and enterprise imaging system in the market in terms of cloud adoption. She said there has been rising interest in adopting cloud over the past few years, and the COVID pandemic showed amity healthcare systems the value of having a cloud-based system for easier remote access to patient data and imaging.

Cloud storage helps solve radiology IT and cybersecurity issues and is growing

Amy Thompson, a senior analyst at Signify Research, explains why radiology is rapidly adopting cloud data storage solutions.

 

An example of artificial intelligence (AI) automated detection of a intracranial hemorrhage (ICH) in. a CT scan used to send alerts to the stroke acute care team before a radiologist even sees the exam. Example shown by TeraRecon at RSNA 2022.

FDA has now cleared more than 500 healthcare AI algorithms

More than 500 clinical AI algorithms have now been cleared by the FDA, with the majority just in the past couple years.

An example of an FDA cleared radiology AI algorithm to automatically take a cardiac CT scan and identify, contour and quantify soft plaque in the coronary arteries. The Cleerly software then generates an automated report with images, measurements and a risk assessment for the patient. This type of quantification is too time consuming and complex for human readers to bother with, but AI assisted reports like this may become a new normal over the next decade. Example from Cleerly Imaging at SCCT 2022.

Legal considerations for artificial intelligence in radiology and cardiology

There are now more than 520 FDA-cleared AI algorithms and the majority are for radiology and cardiology, raising the question of who is liable if the AI gets something wrong.

Brent Savoie, MD, JD, vice chair for radiology informatics, section chief of cardiovascular imaging, Vanderbilt University, explains who will get sued when there is a misdiagnosis due to artificial intelligence (AI).

VIDEO: Who gets sued when radiology AI fails?

Brent Savoie, MD, JD, vice chair for radiology informatics, section chief of cardiovascular imaging, Vanderbilt University, explains who will get sued when there is a misdiagnosis due to artificial intelligence (AI).

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