Imaging Industry Trends… a 2023 Retrospect and 2024 Outlook

The radiology industry continues to play a central role in advancing healthcare. Using imaging equipment such as CT, MRI, and PET, radiologists provide detailed insights into diagnosis and treatments for a wide range of conditions. Radiology also contributes to planning and guiding surgeries, monitoring treatment results over time, and enhancing patient satisfaction and care.

Recent advances in radiology include better image quality, the use of functional data, and enhanced quantitative analysis. The radiology industry embraces transformative technological advancements such as the integration of artificial intelligence (AI) and machine learning into imaging equipment and analytical tools.

In this article, we’ll discuss medical imaging trends in 2023 and consider the 2024 forecast.

Reflecting on Medical Imaging Trends in 2023

Artificial Intelligence and Machine Learning in Radiology

Because radiology is a data-driven specialty, it is well-suited for AI applications. AI was first used in healthcare in 1976 when clinicians used an algorithm to help diagnose intense abdominal pain. Today, the use of AI and machine learning is common across a range of radiology applications.

In 2023, the efforts to integrate AI and machine learning into imaging equipment accelerated, with products boasting built-in capabilities. Examples include:

• GE Healthcare uses deep learning reconstruction in an MRI algorithm to create finer image detail and improved image quality to help clinicians make diagnoses.
• Philips uses an advanced CT reconstruction technique that leverages AI to reduce contrast dose, lower image noise, and improve the detectability of low-contrast.
• Canon is using deep learning reconstruction to enhance spatial resolution and low contrast detectability while at the same time reducing noise – at a speed fast enough for everyday clinical use.
• Siemens Healthineers is developing a prototype of a software assistant for making radiological diagnoses.

The rate of new software radiology products using artificial intelligence is rapidly ramping up. The vast majority of today’s image interpretation software applications tend to fit into three categories:

• Diagnostic
  Functions such as detecting pneumonia on chest radiographs or grading liver tumors.
• Repetitive
  High-volume tasks such as breast or lung nodule detection.
• Quantitative
  Provide calculated results such as lung volume on chest CT for emphysema, bone density measurements, equipment maintenance stats, operational data, and more.

Today, AI is used in healthcare to help detect, classify, and predict diseases. Already, AI is commonly used to detect diseases of the head and neck, breast, chest, and more. Even so, AI is still in the early stages of development in healthcare. New opportunities — such as mitigating workforce shortages, evaluating mental illness, and managing medical triage — are ready to be harnessed.

Predictive Analytics

In 2023, imaging departments and imaging centers saw synergy between innovation and efficiency through the use of predictive analytics. The results for many radiology departments were improvements in operational efficiency and other key performance indicators (KPIs).

Darrin McCall, a diagnostic healthcare executive with over 25 years of experience in imaging operations and Director of Customer Success at Glassbeam, says, “Radiology departments that use analytics to highlight operational efficiencies realize that scheduled exam times have not kept pace with technical advancements of the products. Almost half of radiology exams are being performed in significantly less time than the scheduled exam — presenting an opportunity for improved throughput, scheduling, revenue, and other KPIs. This is a common occurrence with imaging centers we work with.”

Many imaging centers don’t streamline their efficiency because they are unable to quantify what changes they need. From the Example above, without proper analytics, an imaging center may not realize that they typically complete a given exam in less time than scheduled. They can miss the opportunity to harness that extra time to increase the number of profitable exams they fit into the day.

A key component in maximizing operational efficiency is through the reduction of variation. Using predictive analytics can enable leaders to identify patterns, optimize workflows, and standardize procedures through historical data. By better understanding workflow, radiology departments can deliver better patient experiences and financial outcomes.

Patient satisfaction can be improved as well. McCall says, “It all comes down to trust and expectations with the patient. For an open MRI exam with an anxious patient, if you tell the patient that the average exam time is 25 minutes, you save the patient the anxiety of believing they will be in the machine for the full 45 minutes of the scheduled time. But many imaging centers don’t quantify that information and leave it up to the system technologist to communicate with the patient in the room.”

In the past year, predictive analytics tools have become increasingly recognized as critical to understanding patient behavior patterns and imaging department needs. According to McCall, “Predictive analytics can tell you when patients generally arrive late to appointments, and when they tend to arrive early. You can predict how many cancellations to expect.”

By scheduling according to actual time needs, radiology departments can fit in added exams. McCall says, “The organization is paying the overhead and tech salary anyway. Every added scan due to operational and scheduling efficiency is like pure profit.”

Wait time is a big contributor to satisfaction levels. According to a recent poll, 28% of patients admit to leaving the office without seeing the doctor due to long wait times, followed by 26% changing doctors. Additionally, patients warned friends and family not to go to the office, left negative survey reviews, and published negative online reviews, potentially damaging a radiology center’s reputation. Using relevant analytics, radiology departments can streamline operations and minimize wait times, while increasing retention rates, patient satisfaction, and revenue.

Imaging Department Operations and Clinical Services

Operational challenges continued to evolve in 2023, including workforce shortages, mixed-age fleet challenges, and site inefficiencies.

To address the radiology tech workforce shortages and burnout, GE Healthcare received approval for a Digital Expert Access “remote tech” solution. This product promises to help the radiology industry through an innovative approach that allows a single radiology tech to manage multiple locations. With a tremendous shortage of clinical personnel, the industry continues to face a pressing issue, made worse by an aging radiologist workforce looking toward retirement.

Referring physicians are increasingly affiliating themselves with hospital systems, leading to considerations of turnaround times and pricing structures. McCall says, “In many imaging centers, we noted a decline in CT scans. The technology, fees and reimbursements may have a role in influencing these trends. MR and ultrasound experienced an uptick, especially in outpatient imaging centers, where out-of-pocket costs can impact whether a patient selects an in-house versus outpatient site.”

The continued rise of AI and machine learning in 2023 has brought about meaningful advances in research and development, diagnosis, patient prognosis, surgery, and more. Newly available data sets of annotated images have helped advance training and testing. Imaging equipment manufacturers such as Canon, GE Healthcare, Philips, and Siemens Healthineers are focusing on new ways to use AI technology in radiology.

Fueled by advanced algorithms and machine learning, AI has helped bring about advances in DICOM image quality, speed, and interpretation. AI algorithms are helping radiology departments better care for patients while expediting the process. As technology evolves and higher levels of AI performance are achieved, radiologists have taken notice. A recent survey of thoracic radiologists showed that more than 60% expect AI to radically change their practice over the next one or two decades. This same survey reports that more than 80% of these radiologists expect job satisfaction to improve or remain the same.

Currently, many radiology departments are coming to embrace AI to enhance diagnostic capabilities and create a more efficient workflow. This new level of acceptance and use of AI and machine learning is reflected in our 2024 forecast.

Medical Imaging Trends in the 2024 Forecast

Looking forward to 2024, we expect to see accelerating growth in using predictive service analytics to streamline imaging department operational efficiency and to set and track effective KPIs. Utilization analytics will be critical in creating sophisticated planning and streamlined operations to minimize costs and maximize revenue potential. Once considered a standard metric, focusing primarily on the “scans per day” KPI is now considered faulty, and the focus is shifting toward identifying the best mix of scans and payers to maximize revenue.

Large company investments and venture capital funding in AI development of radiology applications is expected to increase, as investors are realizing the potential size of the market. Many small software start-ups as well as major manufacturers of medical imaging equipment — such as Canon Medical, GE Healthcare, Philips and Siemens Healthineers — will continue to develop AI tools that positively impact diagnosis speed and accuracy.

In 2024, we expect DICOM-compatible AI tools to expand into new healthcare applications, as well as improve their diagnosis algorithm accuracy. Radiologist confidence in AI tools is expected to improve over time as well.

Ongoing advancements in AI and predictive analytics in radiology will further refine time-consuming processes, improve the speed and accuracy of diagnosis, and positively influence patient health outcomes. For Example, AI may help enhance the process of identification, severity grading, and clinical prognosis of additional diseases. Quantitative data generated by AI may include useful radiology functions such as calculations of the rate of disease progression from one imaging exam to the next.

Integration of AI tools with DICOM, PACS, imaging equipment, and reporting systems is expected to strengthen in 2024 and beyond, making routine use of quantitative measures and clinical predictive analytics a reality.

Imaging Industry Trends—Conclusion

The journey from 2023 to 2024 promises to be one of adaptation, innovation, and data analysis. Workforce shortage challenges are expected to continue, with additional solutions to this ongoing challenge coming to market. With 2024 being an election year, the radiology industry will brace for a slowdown, while contemplating the potential impact on reimbursement and consolidation. Staying informed and open to new technologies from small software companies as well as large equipment manufacturers such as Canon Medical, GE Healthcare, Philips and Siemens Healthineers will be critical for radiology departments to thrive in this evolving landscape.