An Emerging Utility of AI Radiology: Workflow Optimization

An Emerging Utility of AI Radiology: Workflow Optimization

Radiology is pivotal in modern healthcare, with over four billion procedures performed annually in the USA alone. The demand for imaging services is skyrocketing, while radiology staff and resources remain limited. This surge in demand has led to increased stress and burnout among radiologists, who report high rates of burnout compared to other medical specialties. Fortunately, innovations in AI radiology have opened new doors for efficiency and workflow optimization, which is discussed in a recent fresh-off-the-press review article by Pierre et al. (2023). Classically, AI radiology tools have been focused on and advertised more for the clinical benefit of image interpretation. In this blog post, we will explore how AI radiology services address burnout issues by optimizing workflow, improving image quality, and helping reduce the burden on radiologists. We will highlight current tools, such as AIR Recon DL and Rad AI Nexus, to illustrate how they contribute to workload optimization.

Providing Better Quality Images and Reducing Scan Times

Advances in imaging technology, while groundbreaking, have placed radiologists under the strain of higher workloads, potentially leading to fatigue, errors, and burnout. In this demanding landscape, AI-based tools like AIR Recon DL significantly impact the field by optimizing scanning protocols, reducing scan times, and enhancing image quality. For example, Zaitsev et al. (2016) discusses the persistent issue in magnetic resonance imaging (MRI): the problem of subject motion artifacts. Since its inception as a clinical imaging modality, MRI has grappled with the challenge of motion artifacts, which have become a burden to radiologists who are relied on to provide an accurate interpretation with poor-quality images. While MRI’s sensitivity to particle motion or blood flow can provide valuable image contrast, patients’ involuntary movement often creates significant issues in clinical applications, especially in the setting of long scan times in MRI. In fact, it’s one of the most frequent sources of artifacts in MRI images. 

Researchers have tirelessly explored various methods to mitigate or correct these motion artifacts for over three decades. With the recent advent of deep learning, products such as AIR Recon DL have been trained to produce reconstruction algorithms designed for MRI. AIR Recon DL is not a post-image processing algorithm; it is integrated into the MRI image scanning and reconstruction process. It applies a neural network model comprising over 100,000 unique pattern recognitions. The primary goal is to eliminate noise and Gibbs ringing artifacts in the raw MRI data before forming the final image.

One remarkable feature of this AI tool is its ability to enhance image quality while simultaneously shortening exam times, which has always been a trade-off in the past. It employs a cascade of pattern recognitions to reconstruct only the ideal object image from the noisy data, ensuring that the resulting images are sharper and clearer. It also offers a tunable signal-to-noise ratio (SNR) improvement level, allowing customization to suit users’ preferences. This adaptability is crucial in clinical settings, as it enables radiologists to tailor the image quality to the specific requirements of each case. Additionally, AIR Recon DL incorporates an innovative ringing suppression technology, which identifies common artifacts like Gibbs ringing and truncation and transforms them into improved image details. The end result is an MRI image with higher SNR and spatial resolution, which is invaluable in achieving more accurate and reliable diagnoses with shorter interpretation times.

In the context of addressing motion artifacts, AIR Recon DL’s advanced neural network model, pattern recognition capabilities, and adaptability significantly aid in producing high-quality images, even in scenarios where subject motion could otherwise compromise the diagnostic accuracy. This multifaceted approach, combining ringing suppression and SNR improvement, promises to play a pivotal role in overcoming the persistent challenge of motion sensitivity in MRI, offering radiologists a powerful tool to enhance their diagnostic capabilities and streamline their workflow.

Smart Workload Prioritization and Assignments

Uneven workload distribution and manual data entry have long been recognized as significant contributors to radiologist burnout. Their demanding and often repetitive work can lead to exhaustion and a decline in patient care quality. Our previous blog post discussed how natural language processing aids in report generation and provides clinical history for the radiologist. Moreover, AI-driven workload optimization engines such as Rad AI Nexus tackle this problem by ensuring fair exam assignments, automating repetitive tasks, and streamlining workflow.

Rad AI Nexus introduces automated algorithmic prioritization, a feature that helps streamline the case management process. By automatically ranking cases based on site, subspecialty, and shift responsibility, this tool ensures that the correct study is presented to the right radiologist at the right time. This means urgent cases are promptly handled, and radiologists are assigned cases that align best with their expertise. Moreover, Rad AI Nexus updates the ranking of new studies in real time, ensuring that no case lingers in the queue longer than necessary. Radiologists can now rest assured that they tackle the most critical cases at any given moment, reducing the stress associated with managing large workloads.

One of the most exciting aspects of Rad AI Nexus is its ability to provide actionable insights through AI-driven demand forecasting. By automating the analysis of study volumes and radiologist bandwidth in real time, the platform helps radiology groups make informed decisions about staffing and workflow. Radiologists get the right studies in the best order for them, leading to increased efficiency and reduced stress. Internal studies have shown that radiologists, on average, “save 1-hour a day” with workflow optimization while generating the same amount of wRVU.

Work-Flow Algorithms Can be a Gateway to AI Radiology

Radiologist burnout is a pressing issue that demands innovative solutions. AI radiology services are at the forefront of addressing this challenge by optimizing workflows, enhancing image quality, and streamlining tasks. Radiologists can not only work more efficiently but also provide better patient care. Since these algorithms do not necessarily impact clinical decisions, there is much less liability to implement the service and integrate it into the system. Moreover, the improvement in workflow from these algorithms is much more easily quantifiable, and the cost-to-benefit analysis can be made transparently. On the other hand, the complex nature of quantifying clinical benefit for the hospital system (i.e., how much is the hospital system truly saving money by detecting more cancers to outweigh the investment in AI radiology) is a barrier to AI radiology implementation. Hospital systems and other stakeholders may consider implementing AI radiology in workflow optimization before implementing clinical AI radiology algorithms for these reasons.


Pierre K, Haneberg AG, Kwak S, Peters KR, Hochhegger B, Sananmuang T, Tunlayadechanont P, Tighe PJ, Mancuso A, Forghani R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin Roentgenol. 2023 Apr;58(2):158-169. doi: 10.1053/ Epub 2023 Mar 23. PMID: 37087136.

Zaitsev M, Maclaren J, Herbst M. Motion artifacts in MRI: A complex problem with many partial solutions. J Magn Reson Imaging. 2015 Oct;42(4):887-901. doi: 10.1002/jmri.24850. Epub 2015 Jan 28. PMID: 25630632; PMCID: PMC4517972.

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Brendan Ryu

Brendan is a fourth year medical student applying to radiology residency. He aspires to accelerate MedTech innovation and to build a career integrating innovation into clinical practice.

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