AI provides clinical decision support and workflow improvements

AI provides clinical decision support and workflow improvements

In recent years, substantial progress has been made in the development of AI programs to assist clinicians in their use of diagnostic medical images. Two key areas where AI programs have made a significant contribution are clinical decision support and workflow process improvement.

Clinical decision support

The pace of research and development for new clinical applications of AI is significant and widespread. There are clinical AI programs available for all major diagnostic imaging modalities.

Some feature sets of the AI ​​program that have been used across all modalities are image quality enhancements, lesion detection, and image analysis. Image quality enhancement features focus on improving image reconstruction, reducing image noise, reducing errors and achieving high quality images with lower radiation dose.

Lesion detection features enable early detection of tumors and lesions and disease screening. Image analysis features can provide faster image interpretations, measure and mark lesions, quantify tumor volume, classify abnormalities, diagnose micrometastases, and segment anatomy. The list of new abilities and applications of those abilities to new medical conditions and new use cases seems to be growing by the day.

Workflow process improvements

The increase in volume and complexity of imaging studies contributes to radiologist burnout. AI programs focused on improving image processing time, radiologist reading workflow, scheduling, and patient communication can help mitigate these factors.

Two approaches that have been successfully implemented involve using an AI algorithm to triage imaging exams so that radiologists can focus on the most urgent studies. This approach has been used to reduce recall rates and daily reading lists for radiologists.

Another approach that has been implemented improves the standardization of data and hooking protocols. This has created efficiency improvements for radiologists by allowing them to focus more on reading patient exams.

The integration of AI technology in the field of radiology offers new ways to generate information from medical images and improved processes to manage the challenges created by this new volume of information. As these advances continue and these tools are increasingly adopted in practice, the potential for improving patient care is considerable.

Davin Korstjens is Senior Market Research Program Manager at IMV’s Medical Information Division, part of the Science and Medicine group. IMV’s 2022 Artificial Intelligence in the Imaging Landscape Report explores recent advances at the intersection of artificial intelligence and medical diagnostic imaging. The report covers innovations and applications of AI technology in imaging and companies active in this market.

The 2022 Artificial Intelligence in Imaging Landscape IMV report was released in October 2022 and is based on secondary research and primary research from recent market outlook reports. Vendors covered in this report include GE, Philips, Siemens, etc.

For more information about purchasing IMV’s Artificial Intelligence in the Imaging Landscape Report, visit the company’s website at or call 703-778-3080 ext. 1033 to speak with a representative.

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