AI provides clinical decision support and workflow improvements

In the past several years, there has been significant progress in developing artificial intelligence software to assist clinicians in their use of diagnostic medical images. Two main areas in which AI programs have made significant contributions are clinical decision support and workflow improvements.

Clinical decision support

The rate of research and development for new clinical applications of artificial intelligence is large and widespread. There are clinical AI software available for all major diagnostic imaging modalities.

Some of the AI ​​software feature sets that have been used across the methods are image quality improvements, pest detection, and image analysis. Image quality improvement features focus on improving image reconstruction, reducing image noise, reducing error, and obtaining high-quality images with a lower radiation dose.

The pest detection features enable early detection of tumors, lesions and disease screening. Image analysis features can provide faster image interpretations, measure and mark lesions, determine tumor size, classify abnormalities, diagnose accurate metastases, and segment dissection. It seems that the list of new capabilities and applications of these capabilities for new medical cases and use cases is increasing day by day.

Workflow improvements

The increasing volume and complexity of imaging studies contribute to radiologist fatigue. AI programs focus on improving image processing time, radiologist reading workflow, scheduling, and patient communication, and can help mitigate these factors.

Two methods have been successfully implemented using an artificial intelligence algorithm to sort imaging tests 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 implemented that improves standardization of data and pending protocols. This has led to efficiency improvements for radiologists by allowing them to focus more on reading a patient’s scans.

Integrating AI into radiology is creating new ways to generate information from medical images and improving processes to manage the challenges this new volume of information creates. As these developments continue and as these tools gain more widespread adoption in practice, the potential for improvements in patient care is significant.

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

The IMV’s Artificial Intelligence in Landscape Photography 2022 Report It was published in October 2022 and is based on secondary research and primary research from recent market forecast reports. Vendors covered in this report include GE, Philips, Siemens, and more.

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