Teleradiology Takes Center Stage in Radiologist Shortage

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A person is looking at a brain scan on a computer monitor. The image is in black and white and shows a close up of the brain. The person is wearing gloves and he is examining the scan.
A person is looking at a brain scan on a computer monitor. The image is in black and white and shows a close up of the brain. The person is wearing gloves and he is examining the scan

Radiology Shortages

As the radiology workforce faces critical shortages, teleradiology takes center stage as a scalable solution to help healthcare organizations navigate and overcome the growing radiologist shortage. One solution to help decrease this trend is remote radiology.  With a growing population, over 56.4% of radiologists over the age of 55 (retirement is on the horizon), and a rising demand for imaging services, new solutions must be evaluated to alleviate the shortages being experienced. 

Teleradiology is one solution that can address many critical challenges. A growing shift is moving these services from providing preliminary reads to “final reads”.  In this blog, we explore some emerging trends and best practices that will shape radiology and highlight how Artificial Intelligence (AI) can help.

AI – Radiology’s Savior or Foe?

Radiology has led the way in approvals of AI algorithms cleared by the FDA.  In February 2025, there were over 778 approved algorithms for radiology alone.  But how does an organization or teleradiology practice decide which algorithms are best suited to their practice?

Recently, The Imaging Wire published an article suggesting that while some AI algorithms can improve radiologist workloads, the reality paints a different picture.  A survey published in 2022 polling 185 radiologists, suggested that only 22.7% experienced a workload reduction. Yet 69.8% reported there was no workload reduction at all.  As AI continues to mature, vendors must focus on fine tuning their AI solutions plus shorten the study processing time. Plus, workflow improvements should not further burden radiologists by extending interpretation time.

There are several areas where AI can help improve radiologist productivity in the field of teleradiology.

Subspecialty Matching

Subspecialty matching (matching cases with the appropriate radiologist) is one of the most significant advancements in teleradiology today.  AI would preview an imaging study, evaluate its findings, then match the exam with the appropriate reading specialist.  This model helps:

Health Equity: Rural hospitals often suffer since many times they are not tapped into the best specialists due to their location.  Providing highly specialized reading services for rural locations can improve care access for patients, improve their outcomes, and better normalize care across all imaging locations, regardless of geographic location.

  • Enhance Accuracy: Routing specific studies to the best qualified radiologist, based on their expertise, can improve patient outcomes.  The biggest challenge is ensuring study bias and workload continues to be balanced across the group.
  • Improve Efficiency: Automated matching reduces administrative overhead and accelerates report turnaround times since the best skilled radiologist is the one reading the study.

AI in Clinical Decision Making

Ensuring the correct radiology study was ordered initially goes a long way to reducing the cost of care while improving patient outcomes.  Decision support tools that assist clinicians in ordering the best imaging exam for a given condition can provide faster intervention and improve the patient’s health outcomes.  Modern algorithms can sift through a significant amount of patient history and suggest appropriate imaging exams in seconds, which may take caregivers hours to complete the same task.

AI in General Workflow

Artificial Intelligence solutions can review existing studies and flag/route the most critical cases to the top of a teleradiologist’s worklist.  This ensures faster reading time of studies that require immediate attention.  This not only improves patient care but removes normal studies from critical workflow paths. This allows radiologists to better prioritize their time and ensure critical patients get the care they need faster.

AI based Reporting

Advances in using AI based reporting solutions are improving how radiologists interpret studies.  Solutions by companies like RADPAIR and MD.ai are revolutionizing the way radiologists report studies.  Radiologists can use conversational discussions when reviewing patient exams and the AI understands what is clinically relevant and places that text where it should be placed in the radiology report, speeding up interpretation time.  Additionally, flagging clinical errors (like ensuring the dictated study matches the order – especially for left/right body parts) improves clinical accuracy. 

Providing real-time access to medical databases optimizes clinical follow-up, providing more value in final reports sent to the patient’s clinician.  Finally, AI can help add or identify relevant information from the EHR or prior exams as it relates to the patient’s history, ensuring radiologists focus on the most critical aspects of documented findings in a patient’s clinical evaluation.  Auto-impressions and routing reports for appropriate follow-up (flagging critical results) speeds up patient care intervention when needed.

Technology Can Save the Day

Technology is one way to combat the radiologist shortage. Teleradiology is an emerging workflow change that also alleviates bottlenecks and balances workloads.  By providing preliminary, after-hour coverage, peak time coverage, and final reads, teleradiology provides a host of benefits to hospitals struggling to attract qualified radiologists to support their imaging volumes.  Additional benefits include:

  • 24/7 Coverage: Teleradiology groups can provide remote reading services around-the-clock, alleviating the burden on in-house teams.
  • Load Balancing: Teleradiology networks can distribute workloads more evenly across time zones and regions with the radiologists they have contracted with.  Not only can this better distribute the workload, but it can also get the right exam to the right radiologist at the right time.
  • Rural Healthcare Support: Remote areas can now gain access to a pool of expert radiologists they would not normally have access to.  This can improve the diagnostic capabilities in underserved regions and ultimately provide enhanced patient care since studies with their condition (or subspecialty routing) can get routed to the radiologist who has the highest level of expertise with their condition or subspecialty.

Earlier in this blog, we mentioned the surprising findings that only a small percentage of radiologist’s experience productivity gains with technology, yet technology is required to accommodate the field of teleradiology.

Easy to use PACS solutions, automated study routing, AI-based exam assignments and preliminary study review, and AI-based report creation, are just a few areas of technology that can help drive better efficiency, speed, and accuracy.  Cloud-native PACS solutions allow anytime, anywhere access to imaging studies and is required for teleradiology to be efficiently managed.

Teleradiology – Better for Patients?

Not only can teleradiology improve handling imaging volumes but patient care can also benefit from this technology.

  • Rapid Turnaround Times: Oftentimes, faster diagnoses can occur since teleradiology groups may “follow the sun”, meaning staffed reading services are provided virtually 24×7. This leads to quicker treatment initiation and improved patient outcomes.
  • Second Opinion Services: Since the pool of radiologist expertise is greatly enhanced with teleradiology services, patients may have easier access to multiple expert opinions, enhancing diagnostic accuracy.
  • Integration with Patient Portals: Most PACS solutions provide some form of a patient portal.  Providing patients with direct access to their radiology reports and images improves engagement and understanding.  Some reporting solutions even produce patient friendly reports, further enhancing understanding.

Improving Work/Life Balance

One of the highest complaints among staff radiologists is burn-out.  Radiologists are burdened with tasks that do not directly relate to patient care and reading studies, causing lower productivity and a decrease in job satisfaction, which ultimately leads to burnout.  Teleradiology improves work/life balance by providing:

  • Flexible Scheduling: Radiologists can choose the hours they want to work and manage a schedule around their specific lifestyle, which can significantly improve work/life balance.
  • Remote Work Opportunities: The ability to work from any location can significantly expand career options and job satisfaction for many radiologists.  Not having to be present daily at a hospital may also increase efficiency and allow more studies to be read during a typical shift.
  • Reduced On-Call Burden: Again, since many teleradiology groups have a “follow the sun” business model, distributing specialists across multiple time zones can lead to more manageable schedules, reduced or no on-call responsibilities, and better workload management.

Best Practices for Teleradiology Success

To fully leverage these trends, healthcare organizations and radiologists should consider the following best practices:

  1. Invest in Robust IT Infrastructure: Ensure high-speed, secure connections and state-of-the-art Cloud-Native PACS systems to support seamless remote reading while providing enhanced data security.
  2. Prioritize Cybersecurity: Working with a cloud provider can provide better cybersecurity than on-premises solutions.  Using remote reading services is imperative that elevated security practices are followed and implemented to protect sensitive patient data.
  3. Quality Assurance: Establish rigorous QA processes at contracted hospitals to ensure the highest image quality is obtained the first time.  This leads to better diagnostic accuracy in remote settings and reduces patient call backs.
  4. Embrace AI Integration: Adopt AI tools that focus on workflow optimization, preliminary reads, and decision support to enhance efficiency and accuracy.  Make sure that any AI algorithms chosen are the right ones for your organization and work well with your patient population mix.
  5. Foster Communication: Implement systems that facilitate quick and clear communication between remote radiologists, referring physicians, staff technologists, and patients.  Keeping everyone in the loop and providing tools to allow easy communication can further improve patient care.

Conclusion

Teleradiology is one accepted method that can help address the radiologist shortage and expertise gap that plagues our industry today, while providing the ability to improve patient care and professional satisfaction. By embracing these trends and implementing best practices, healthcare organizations can position themselves to maintain high standards and potentially improve report turnaround times.

The teleradiology market is expected to continue to grow at a CAGR of 19.7% from 2024-2032. By providing an environment without geographic boundaries and properly outfitted with today’s leading technology, limitations to expert radiological care becomes a thing of the past.

InsiteOne offers a robust teleradiology solution that scales anywhere from small reading groups to large teleradiology practices with ease. Providing simplistic, yet effective workflows for small groups to offering AI integrated platforms, advanced AI reporting options, and optimized workload balancing for large practices, we can offer solutions that fit virtually any practice need and size. If your group is considering investing in new technology to help grow your teleradiology practice, be sure to reach out to the InisteOne team today to learn how our solution can help set new levels of productivity and efficiency for your teleradiology group today!

AI Mammography: Revolutionizing Breast Cancer Detection with Advanced Technology

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Introduction

AI use in Mammography offers a leap forward in revolutionizing women’s health. Mammography screening is a proven path to early breast cancer detection and improved survival rates and is crucial for optimal women’s health. Recent studies have revealed remarkable effectiveness, particularly among different age groups, with screening averting 6.7 breast cancer deaths per 1,000 women in the general population and an even more significant impact of 9.2 deaths averted per 1,000 Black women screened.

The latest research from the U.S. Preventive Services Task Force demonstrates that starting regular screenings at age 40 can make a substantial difference – for women aged 60-69, screening just 377 individuals can save one life, while this number increases to 1,339 for women aged 50-59. These compelling statistics underscore why healthcare providers strongly advocate for regular mammography screening as a vital tool in the fight against breast cancer, particularly given that early detection significantly expands treatment options and improves overall outcomes.

Given the known effectiveness of mammography screening, Artificial Intelligence (AI) has emerged as a game-changer in identifying women early on that are at an increased risk of future disease. AI is not a replacement to the effectiveness and experience of trained radiologists reading studies, but compliments their expertise and acts as an aid, helping them reduce false negatives and positives while reducing call-backs and biopsies. 

As imaging centers strive to provide the best possible care while maintaining financial viability, AI in mammography can become an invaluable asset in today’s mammography screening programs. This blog will explore how AI is not only improving early detection rates but also boosting practice profitability when strategically implemented.

Understanding AI Mammography and Patient Benefits

The primary goal of any mammography screening program is the early detection of breast cancer in women. One area that AI can help is in improving the sensitivity and specificity of the exams. 

AI algorithms can analyze a much higher volume of imaging data than a radiologist can in a typical workday, therefore, the outcome is that AI tools can reduce the time it would take for radiologists to review the same number of images.  Since most AI algorithms are trained with a very high volume of data, these algorithms often can detect abnormalities that may be missed by the human eye earlier in the disease process.  This can result in:

  • Lower interval cancer rates
  • Fewer patient recalls for additional views or biopsies
  • More effective treatment options since cancers can be detected earlier

Since a consensus conference is usually required to achieve a high sensitivity and specificity rate, the implementation of AI can help improve this process while freeing up radiologist’s time to focus on more critical cases.

Another area where AI in mammography can help is its ability to minimize false positives. With a reduction in false positives, this can lead to:

  • Fewer unnecessary follow-ups, which ultimately can reduce patient anxiety, free up resources to focus on net new patients, and improve overall operations
  • A decrease in avoidable biopsies, saving both time and resources.  In the US alone, about 10% of women are recalled for further testing, yet only about 0.5% of these women who undergo testing have a positive cancer outcome. 

According to the National Cancer Institute, between 20%-50% of screen detected cancers are overdiagnosed, creating undue anxiety in women after informed of a false positive result.

AI Mammography Radiologist Benefits

Beyond improving detection rates, AI tools are revolutionizing workflow in imaging centers in multiple areas that provide a real impact on staffing and center optimization.  Some areas where AI can improve workflow include:

  • Faster Reading Times: AI can review far more cases than a radiologist can in a given time period.  Allowing AI to read cases that are possibly normal can free up radiologist time to read more critical cases.  Additionally, when radiologists overread what the AI has performed, they can navigate through the images faster, increasing their overall efficiency.
  • Reduced Burnout: By automating time consuming and repetitive tasks during the image analysis process, AI can reduce radiologist fatigue.  Pulling patient history, ensuring the latest exam is used for comparison, adding follow-up exams to a worklist, and a number of other workflows can provide more time the radiologist has to review patient images.
  • Prioritization: After analyzing imaging data sets, AI can flag high-risk cases, ensuring they receive immediate attention from the radiologist.  Using advanced worklists can help filter these high-risk cases and often ensuring the right exam goes to the radiologist with the highest level of expertise in each condition.  This improves patient outcomes and ensures priority cases are given the highest priority.

These workflow improvements not only enhance the quality of care but also contribute to increased throughput within the organization, which is one of the key factors in improving practice profitability.

Pros and Cons of AI Mammography

As with any technology, there are pros and cons to using these advanced tools.

Some of the Pros of using AI in mammography and medical imaging include:

  • Improved detection rates
  • Reduced workload for radiologists
  • Potential for earlier diagnosis

While some of the Cons you should consider before implementing AI in your imaging organization include:

  • Initial implementation costs can be costly offsetting the time to realize an effective ROI
  • Need for ongoing algorithm updates to keep the AI tool continually “learning”. This can be a laborious task and if on-going updates are ignored, the accuracy of your AI algorithm could diminish.
  • There is always the potential for over-reliance on technology. AI is a tool to assist radiologists in image interpretation, not a replacement. Over-reliance on AI could lead to some findings going unchecked.

Investing in AI for Long-Term Gains

While the initial investment in AI technology may seem substantial, the long-term financial benefits are can be quite compelling, if the right technology is matched properly to the right facility.  Below are a few financial areas to consider when evaluating AI:

Return on Investment (ROI)

At first glance, most AI algorithms do not qualify for reimbursement.  With so many algorithms available, yet lacking in reimbursement, how can an imaging center create a strong ROI to move forward with clinical AI tools?  The combination of increased efficiency, reduced liability costs, and improved patient outcomes do contribute to a healthy bottom line, but many of those are soft costs that are hard to realize when confronted with the monthly fees associated with many AI solutions.  Keep reading to understand how InsiteOne is helping to improve AI availability while keeping costs of those solutions in check.

Innovative Reimbursement Strategies

As part of the adoption process for AI, many imaging organizations are taking a creative approach to offering these solutions, while offsetting the costs to boost revenue. 

  • Opt-in AI Fee: Voluntary opt-in fees are starting to appear in some of the country’s most prominent imaging centers.  These fees typically range from $35-$70, and are paid for in cash, for the patients who choose to have their mammograms analyzed with AI. This approach not only covers the cost of the software but also emphasizes the added value to patients.  This approach works well in populations where your patients would be willing to have AI “read” your images in conjunction with a radiologist.  InsiteOne can help with the modeling of how adding AI into your practice on a fixed monthly cost can be offset by these cash payments, which can improve overall center revenue.

Expanded Service Offerings

Another area and/or benefit that AI can provide is expanding the service offerings you provide to your patients.  If your center only performs mammography studies, other AI algorithms can be added and often can detect subtle findings on mammography studies that could be an indicator for other health problems, such as: 

  • Cardiac Screening: By leveraging AI capabilities, centers can offer cardiac screenings to detect early signs of coronary artery disease, creating a new revenue stream.

This can provide patients with advance notice and allow early treatment pathways to be established to improve their overall health, while reducing the future burden on the health system, since early detection is often far more cost effective to treat than late-stage detection.

The InsiteOne and NTT DATA Advantage

To fully harness the power of AI in mammography, many imaging centers are turning to partnerships with industry leaders. The collaboration between InsiteOne and NTT DATA offers a comprehensive solution that addresses both the technical and practical aspects of AI implementations:

  • Seamless Integration: InsiteOne’s expertise in RIS, PACS, and data storage, all as a fully managed service, ensures that AI tools integrate smoothly with a site’s existing RIS/PACS system, or a solution offered by InsiteOne.  InsiteOne brings the expertise of systems integration and managed services, which allow your center to focus on patient care, all while paying a reasonable monthly fee.
  • Cutting-Edge Algorithms: NTT DATA’s expertise lies in procuring advanced AI capabilities as part of an integration platform that doesn’t’ limit scalability or growth, should your organization want to expand your AI program in the future.  With state-of-the-art detection and analysis tools at their disposal, NTT DATA has the expertise to provide you with guidance on what the best algorithms will be for your organization that provides the highest adoption rate for your patients.
  • Data Security and Compliance: Again, with InsiteOne’s long-term focus on robust data storage solutions, robust security is not a second thought in any of our services or offered solutions.  Your organization can confidently adopt AI technology while maintaining regulatory compliance and know that your patient data is safe and secure.

Conclusion: A Brighter Future for Women’s Health

Last month, InsiteOne rolled out our AI Powered Mammography program in collaboration with NTT DATA.  This Insider Session was recorded and offers more information on the benefits of this program to imaging locations looking to capitalize on the use of AI in their practice, but not sure where to turn or what to choose.  By improving detection rates, streamlining workflows, and opening new avenues for profitability, AI is not just a technological advancement – it’s a transformative force in healthcare.

If you would like to learn more about this program, the benefits, costs, and potential revenue increase you could incur, reach out to the team at InsiteOne and start a conversation.  Adopting AI in your practice is not a one-time initiative but rather a journey that benefits from the expertise organizations like InsiteOne and NTT DATA have in this industry and can be offered to you through a long-term, consultative approach as the AI in healthcare industry continues to evolve.

Start by downloading the mammography Insider Insight video and then take the next step and reach out to InsiteOne to get more information on our pilot program and how it could benefit your organization and patients care.