Radiology is a crucial component of modern healthcare, enabling clinicians to visualize and diagnose a wide range of medical conditions. In recent years, the field of radiology has undergone significant technological advancements that have revolutionized the way medical professionals diagnose and treat various illnesses. In addition to these technological advances, there has been a growing emphasis on driving efficiency and improved productivity across radiology departments. Radiology department leaders and administrators are exploring new ways to improve clinical outcomes while optimizing operational efficiency.
Advanced radiology and innovative technologies have emerged as promising solutions to drive productivity, monitor quality, and improve outcomes across the radiology department with the goal of amplifying efficiencies to achieve a more streamlined patient care journey. This has become particularly important as the demand for radiological services continues to grow. Simultaneously, radiology administrators are challenged with high staff turnover, staffing shortages, and high levels of burnout.[1]
Industry partners such as GE HealthCare are committed to augmenting the power of advanced radiology with optimized clinical workflows and artificial intelligence (AI) solutions to help healthcare providers deliver better patient care while streamlining workflows and increasing productivity.
“We’re developing solutions to integrate innovation and AI to help streamline the clinical workflow, increase efficiency, and improve image quality,” said Scott Miller, Global Imaging Chief Marketing Officer for GE HealthCare. “The real value of driving improved efficiencies through innovative AI is ultimately to provide the clinical insights necessary to improve clinicians’ diagnostic accuracy and deliver better clinical outcomes for patients.”
Improving outcomes with modern imaging tools
In recent years, modern imaging tools, such as advanced radiology techniques, imaging reconstruction with deep learning, and clinical applications using AI, have revolutionized patient care. These tools help improve the accuracy and speed of diagnoses and enable clinicians to make more informed treatment decisions. With the help of these imaging techniques, healthcare providers can optimize personalized treatment plans to each patient's unique needs and offer more effective, targeted therapies.
To aid in image reconstruction, a range of AI-based image analysis models, such as machine learning, have been developed. Applying AI and deep learning image reconstruction is an exciting realization of modern technology, enabling image quality improvements that haven’t been possible using traditional imaging reconstruction methods. Radiology staff and clinicians are using these new technologies, available in imaging modalities including X-ray, magnetic resonance imaging (MRI), computed tomography (CT), and molecular imaging (MI), to consistently produce high-quality images, often with shorter scan times, and help improve the patient experience during the exam.
- GE HealthCare’s deep learning MRI reconstruction technology is a trained algorithm to reconstruct sharper images by leveraging all the raw MRI acquisition data while enabling shorter scan times.
- GE HealthCare's deep learning image reconstruction engine in CT applications was trained with a vast library of low-noise, filtered back projection images that are recognised as the gold standard for image quality. The implementation of these deep neural networks yields high-quality images with reduced noise and artifacts, enhancing the image quality and resolution.
With the successful implementation of deep learning in X-ray, CT, and MRI, a new frontier of machine learning is now being explored in positron emission tomography (PET)/CT to improve image processing capabilities within molecular imaging, presenting a crucial application of AI.
Enabled by molecular imaging technologies, Theranostics relies on high-quality imaging and is paving the way to further treatment of advanced-stage cancer types. By incorporating deep learning image processing in MI, it can help deliver clearer images and greater diagnostic confidence and Theranostics continues to further strengthen its potential. With current successful applications treating metastatic prostate cancer and neuroendocrine tumors, the Theranostics approach represents a new level of personalized care and the future of therapeutics for many potential new applications.
Using AI and automation for workflow efficiency in medical imaging
As radiology technologies become more advanced, the imaging workflow can be time-consuming and resource-intensive. To address these challenges, AI and automation solutions help drive efficiency in the imaging workflow. By integrating AI to optimize image acquisition and using tools to automate repetitive tasks, radiology departments can:
- Achieve high-quality diagnostic images
- Streamline the exam process
- Accommodate more patients
- Improve patient satisfaction and clinical outcomes
AI and machine learning have demonstrated their effectiveness in radiology, as this technology can help automate repetitive tasks, monitor quality during imaging acquisition, detect intricate abnormal patterns in image data, and aid clinicians in diagnosing patients.
Driving efficiency with AI and automation in X-ray
Industry leaders have developed AI and automated tools across imaging systems. Within recently developed X-ray systems, the ergonomic and user-centric design leverages automation to aid technologists in ensuring proper anatomical positioning, quality control, and confirming protocol selections during scanning. Additionally, images are auto-rotated to save radiologists time. The on-device AI algorithm analyses images immediately upon acquisition for urgent cases such as pneumothorax, and triage notifications are presented to support prioritised review. The aim is to enhance the triage of critical findings and improve the efficiency of the radiology process.
X-ray image quality can also be enhanced with AI in image processing. This can be done by increasing the brightness and contrast in images, making them more detailed and easier to see. AI can also be used to sharpen images, making them clearer and easier to interpret. By integrating robust AI solutions in X-ray, technologists can leverage the full advantage of the resolution in the X-ray detectors to adjust how the X-ray image is presented to reading radiologists. AI-driven automated brightness and contrast levels can help drive consistency across all X-ray images.
Effortless workflow with AI and automation in CT
Take the CT experience to a new level of speed and precision with advanced CT technologies that automate and simplify time-consuming tasks from pre-scan to post-scan. With optimized protocols and expanded patient positioning options that can allow for increased patient throughput, a suite of AI solutions help personalize scans accurately and automatically for each patient. Consequently, due to a significant reduction in the number of clicks required on equipment and workstations, technologists can position patients with a high level of precision, choose appropriate protocols based on automated suggestions, and potentially perform examinations in less time.
When AI technology is utilised to facilitate user-centered design, it can function as a virtual aid, automating and assisting technologists, thereby minimizing physical labor and increasing operational efficiency. This, in turn, allows technologists to spend more time at the patient's bedside, enabling them to provide optimal patient care.
Using radiology management tools for optimal productivity and consistency
Radiology department managers play a critical role in ensuring the smooth and efficient operation of medical imaging departments. With the increasing complexity of imaging technologies and the growing demands for high-quality imaging services, effective management tools have become essential to help leaders and administrators gain a comprehensive understanding of their department's operations and performance.
Understanding the interdependency of the variables within the radiology department can provide actionable insights across multiple functional areas. By using imaging department management tools, radiology department managers can gather and analyse data on system utilisation, image quality, protocol optimisation, and staffing across locations. The ability to identify key levers to affect change can be an effective mechanism to keep the department on track. This information can help managers identify areas of improvement, optimize resource allocation, and enhance the overall quality of imaging services.
The quality of medical images is essential for effective patient care. Though each imaging exam may require a certain level of personalization to meet patient needs, limiting variability in imaging is key to increasing consistency across the radiology department and across imaging operations. A lack of visibility and management of imaging variability can also lead to issues for referring physicians.
After streamlining the workflow with automation and AI, radiology administrators can use protocol management tools to ensure high image quality, no matter the experience level of the technologists. Protocol standardization can help optimize radiology operations, beginning with a focus on consistency in ordering, acquisition, and appearance of a study—regardless of location. It can also help eliminate the manual process of updating scanner protocols to keep protocols up to date. Protocol management solutions can enable automatic distribution and edit imaging protocols remotely, helping provide consistent, superior image quality across devices and locations.
Driving success in radiology by leveraging technology and innovation
The success of any radiology department hinges on the ability to leverage technology and innovation in a strategic and meaningful way. By adopting a holistic approach to radiology management and embracing the latest advancements in AI and imaging technology, departments can drive better outcomes for both patients and staff alike.
GE HealthCare is committed to enabling clinicians’ continued diagnostic accuracy with advanced radiology, streamlined workflow, and integrated AI solutions.
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DISCLAIMER
Not all products or features are available in all geographies. Check with your local GE HealthCare representative for availability in your country.
REFERENCES
[1] Radiologist burnout. Catalina Imaging. https://catalinaimaging.com/radiologist-burnout/. Accessed April 26, 2023.
