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.

AI detects twice as many incidental lesions as radiologists
Prior research has suggested that these incidental lesions turn out to be cancerous in up to 70% of cases, making their timely detection critical.
New teleradiology solution promises to streamline enterprise imaging operations
Konica Minolta and NewVue announced their collaboration on cloud-based Exa Teleradiology earlier this week.
Fine-tuned LLMs show great promise for spotting radiology report errors
Report errors can result in incorrect or delayed diagnoses and significantly impact disease management.
Predictive model cuts back on unnecessary imaging requests for headaches in the ED
Headaches are a common complaint among patients presenting to the emergency department, with millions of cases documented annually in the United States.
First-of-its-kind framework monitors real-time performance of radiology AI tools
The program provides detailed assessments of how algorithms implemented into workflows are impacting efficiency and outcomes.

How one radiology department increased use of its alert system for critical imaging findings

Almost 10% of radiology reports contain such an alert and communication of them is crucial to keeping patients safe and avoiding malpractice lawsuits.

Example of natural language processing converting the radiologist's dictation into text. This system from M-Model highlighted key words the artificial intelligence will use text in the report and for labeling the report file for later key word searches or data mining.

How NLP can 'revolutionize' structured reporting

The continued emergence of natural language processing has caught the eye of experts in the field, with some suggesting its use could streamline the process of integrating structured reporting across the specialty.

Traditional methods continue to outperform AI in some orthopedic scenarios

A new meta-analysis suggests that when it comes to hip fractures, AI algorithms do not always live up to their hype.

Russian-speaking ransomware group leaks breast cancer patients' sensitive data

"We have been in your network a long time and have had time to study your business. We have stolen your confidential information and are ready to publish it," the ransomware group threatened.

Closing the loop and settling rare clinical disagreements between radiologists at the same institution

Oftentimes, these deviations can occur informally, but experts believe careful documentation is needed to resolve them in a timely fashion.

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.

KLAS 2023 rankings for cardiovascular information systems and hemodynamic solutions

Hospital end-users ranked the CVIS and hemodynamic systems they are using and shed light on their working relationships with IT vendors.

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.

Imaging Informatics

AI detects twice as many incidental lesions as radiologists

New teleradiology solution promises to streamline enterprise imaging operations

Fine-tuned LLMs show great promise for spotting radiology report errors

Predictive model cuts back on unnecessary imaging requests for headaches in the ED

First-of-its-kind framework monitors real-time performance of radiology AI tools