Tag: radiologists

CT Guides Heart Health

Posted on by Brian Casey

People who got evidence of their cardiovascular health from coronary CT angiography scans led healthier lifestyles compared to those who got conventional cardiac risk scoring. That’s according to a new study in JAMA Cardiology that has intriguing ramifications not only for managing heart disease but also for the imaging-based wellness industry. 

Cardiovascular disease is the number one killer globally, accounting for one in seven deaths.

  • The risk of heart disease can be managed through lifestyle changes like better diet and exercise, but getting patients to follow their doctors’ advice can be a challenge.

So researchers in the new study investigated whether data from a patient’s own coronary CT angiography exam would be a better motivational tool compared to simply calculating a risk score based on demographic factors like weight, BMI, and daily step count.

  • They drew 400 participants from the SCOT-HEART 2 study of CT-based cardiovascular screening in Scotland. 

Cardiovascular risk scores were calculated for one group using the ASSIGN criteria for 10-year cardiac event risk, while another group got CCTA scans and were shown their results. 

  • Interventions were recommended for patients in either group based on cardiac risk as calculated by either ASSIGN criteria or CCTA scans, ranging from no interventions to low-level statin therapy to high-intensity statin and enzyme inhibitor treatment.

At six months of follow up, researchers calculated how many participants met the U.K.’s NICE recommendations for diet, body mass index, smoking, and physical exercise, finding …

  • Nearly three times more CCTA patients complied with NICE healthy lifestyle guidelines (17% vs. 6%).
  • Fewer CCTA patients were told to start preventive therapy due to their risk (51% vs. 75%).
  • And of these, CCTA patients were more likely to have followed advice to begin a therapeutic program (77% vs. 46%). 
  • There was no difference in behavior between CCTA patients who saw their own images and those who were told verbally of their results.

In one important fact, the researchers noted that the study was only designed to measure compliance. 

  • It did not assess any change in coronary events over time – these will be addressed in the larger SCOT-HEART 2 study. 

The Takeaway

The new study offers powerful evidence that getting their own medical imaging results can drive patients to adopt lifestyle changes that lower their disease risk. In addition to informing cardiovascular disease management, it’s also possible to see these findings employed as part of the wellness screening programs that are becoming increasingly prevalent. 

All-Star AI for Prostate MRI

Posted on by Brian Casey

An AI model for prostate MRI that combines the best features of five separate algorithms helped radiologists diagnose clinically significant prostate cancer in a new study in JAMA Network Open. 

The Prostate Imaging-Cancer AI consortium was formed to address a nagging problem in prostate cancer screening.

  • Studies have shown that MRI can reduce biopsies and minimize workup of clinically insignificant disease, but it also has high inter-reader variability and requires a high level of expertise. 

The PI-CAI challenge brought together researchers from multiple countries with a single goal: develop an AI algorithm for prostate MRI that would improve radiologists’ performance.

  • Results were presented at RSNA and ECR conferences, as well as in a 2024 paper in Lancet Oncology that showed that individually the algorithms improved radiologist performance and generated fewer false positives.

But what if you combined the best of the PI-CAI algorithms into a single all-star AI model? 

  • Researchers did just that in the new study, combining the top five algorithms from the PI-CAI challenge into a single AI model in which each algorithm’s results were pooled to create an average detection map indicating the presence of prostate cancer. 

To test the new algorithm, 61 readers from 17 countries interpreted 360 prostate MRI scans with and without the model. 

  • Patients in the test cohort had a median age of 65 years and a median PSA level of 7.0 ng/mL; 34% were eventually diagnosed with clinically significant prostate cancer.

Results of PI-CAI-aided prostate MRI were as follows …

  • Radiologists using the algorithm had higher diagnostic performance than those who didn’t (AUROC=0.92 vs. 0.88).
  • PI-CAI working on its own had the highest performance (AUROC=0.95).
  • Sensitivity improved for cases rated as PI-RADS 3 or higher (97% vs. 94%).
  • Specificity also improved (50% vs. 48%).
  • AI assistance improved the performance of non-expert readers more than expert readers, with greater increases in sensitivity (3.7% vs. 1.5%) and specificity (4.3% vs. 2.8%).

The Takeaway

The new PC-CAI study is an important advance not only for prostate cancer diagnosis but also for the broader AI industry. It points to a future where multiple AI algorithms could be combined to tackle clinical challenges with better diagnostic performance than any model working alone.

RadGPT Simplifies Radiology Reports for Patients

Posted on by Brian Casey

When it comes to informing patients of their imaging results, radiologists are caught between a rock and hard place. A new study in JACR shows how generative AI can help by drafting patient-friendly reports that are simple but accurate.

Patients must be informed immediately of their medical results according to a 2021 final rule under the 21st Century Cures Act that prevents medical information blocking. 

  • And while the technology exists to do that through tools like email and electronic patient portals, rapid notification can create confusion because the language physicians use to communicate with each other isn’t easily understood by anyone outside medicine.

Sure, radiology reports could be rewritten manually for patients, who typically read at about the eighth-grade level.

  • But given today’s workforce shortages, who’s going to do that?

Generative AI and large language models offer a solution. In the new JACR paper, researchers from Stanford University led by senior author Curtis Langlotz, MD, PhD, described their development of RadGPT, an LLM designed to improve patient communication.

  • To develop RadGPT, researchers started with OpenAI’s GPT-4 model and the RadGraph concept extraction tool to create an LLM that analyzes patient radiology reports and generates concept explanations and question-and-answer pairs.

How well did RadGPT work? The researchers tested it on 30 radiology reports generated at Stanford from 2012 to 2020, including different modalities and clinical applications. 

  • The LLM was asked to generate reports at a fifth-grade reading level (the level recommended by the Joint Commission for patient-facing healthcare materials).

Five radiology-trained physicians then rated the quality of RadGPT’s responses, finding …

  • The average rating of RadGPT-generated concept explanations was 4.8 out of 5.
  • 95% of concept explanations had an average rating of 4 or higher.
  • 50% of concept explanations were rated 5, the highest possible rating.
  • Questions and answers generated by RadGPT were also rated highly, with an average rating of 3.0 on a three-point scale..

The Stanford researchers told The Imaging Wire that their goal is to make RadGPT more widely available as part of a prospective evaluation with real patients.

  • They are also developing a user-friendly interface in which patients can receive hyperlinked radiology reports.

The Takeaway

RadGPT and solutions like it fill a desperate need for tools that can save time for radiologists while helping patients better understand their reports and get more engaged in their care. The next step is to get technology like this into the hands of practicing radiologists.

Mammo Risk Prediction Improves with AI

Posted on by Brian Casey

Artificial intelligence is beginning to show that it can not only detect breast cancer on mammograms, but it can predict a patient’s future risk of cancer. A new study in JAMA Network Open showed that a U.S. university’s homegrown AI algorithm worked well in predicting breast cancer risk across diverse ethnic groups. 

Breast cancer screening traditionally has used a one-size-fits-all model based on age for determining who gets mammography.

  • But screening might be better tailored to a woman’s risk, which can be calculated from various clinical factors like breast density and family history.

At the same time, research into mammography AI has uncovered an interesting phenomenon – AI algorithms can predict whether a woman will develop breast cancer later in life even if her current mammograms are normal. 

The new study involves a risk prediction algorithm developed at Washington University School of Medicine in St. Louis that uses AI to analyze subtle differences and changes in mammograms over time, including texture, calcification, and breast asymmetry.

  • The algorithm then generates a mammogram risk score that can indicate the risk of developing a new tumor.

In clinical trials in British Columbia, the algorithm was used to analyze full-field digital mammograms of 206.9k women aged 40-74, with up to four years of prior mammograms available. Results were as follows …

  • The algorithm had an AUROC of 0.78 for predicting cancer over the next five years.
  • Performance was higher for women older than 50 compared to 40-50 (AUROC of 0.80 vs. 0.76).
  • Performance was consistent across women of different races.
  • 9% of women had a five-year risk higher than 3%. 

The algorithm’s inclusion of multiple mammography screening rounds is a major advantage over algorithms that use a single mammogram as it can capture changes in the breast over time. 

  • The model also showed consistent performance across ethnic groups, a problem that has befallen other risk prediction algorithms trained mostly on data from White women. 

The Takeaway

The new study advances the field of breast cancer risk prediction with a powerful new approach that supports the concept of more tailored screening. This could make mammography even more effective than the one-size-fits-all approach used for decades.

Cancer Screening at ASCO 2025

Posted on by Brian Casey

The American Society of Clinical Oncology’s annual meeting isn’t usually known for diagnostic radiology research. But this week’s conference in Chicago included a number of radiology-related studies, particularly regarding cancer screening. 

Most ASCO meetings are dominated by new chemotherapy advances. 

  • But oncologists maintain a strong interest in cancer screening as the first step to guiding patients into advanced treatments.

At ASCO 2025, screening disparities were at the top of the agenda, as evidenced by the following presentations …

  • Mobile mammography addressed healthcare disparities for both urban and rural women in a study that analyzed demographics from 8.3k women screened. 
  • Patients served by mobile mammography in Pennsylvania were more likely to be Black (68% vs. 40%), uninsured (71% vs. 2.1%), and live in an economically deprived area (70% vs. 27%), and they also had higher recall rates (19% vs. 9.9%) and twice the median days to case resolution (29 vs. 14 days).
  • U.S. women who didn’t get mammography screening tended to be younger, uninsured, and have issues with medical costs.
  • Farther afield, Uzbekistan’s new breast screening program was described, with 83.6k women screened and 80% of cancers detected at an early stage.
  • The program also uses AI, with AI achieving higher AUC than a three-radiologist average (0.89 vs. 0.82) while reducing workload 41% with 3X lower recall.
  • In Saudi Arabia, AI was used to audit mammography reports for quality and compliance with BI-RADS guidelines. 
  • A virtual-first approach in California successfully reached candidates for colorectal and CT lung cancer screening, using an online platform with educational resources and scheduling. Of 71 people who met lung screening criteria, 24% completed CT scans, and of these 29% had clinically significant findings. 
  • To improve CT lung screening among low-income people of color, Indiana researchers enrolled 89 screening-eligible people in an educational program. Before the program 56% had never heard of lung cancer screening, but afterwards 100% said they believed screening could save their lives.
  • Ohio researchers found that of 116 lung cancer cases in a tumor registry, 24% got low-dose CT lung screening.
  • An IT tool detected patient concerns about screening’s cost – AKA financial toxicity – and assigned financial navigators to help them. 

The Takeaway
This week’s ASCO 2025 sessions demonstrate the synergy between screening and treatment that’s improving survival for a broad spectrum of cancer patients. Continued progress will only serve to benefit both disciplines.

CT Use Linked to Higher Radiation Exposure

Posted on by Brian Casey

A new study revisits the debate over CT radiation risk, finding a link between greater use of CT scanning in a country and the percentage of patients getting higher cumulative doses of radiation over time.

Managing medical radiation has been a priority for decades, but the issue gained new prominence in April with the publication of a controversial paper linking CT use to future cancers. 

  • Critics accused study authors of sensationalizing the radiation dose issue, but researchers pointed out that they used existing models for radiation dose and cancer risk.

Enter the new study, in which a team led by international radiation safety expert Madan Rehani, PhD, calculated the number of patients getting over 100 mSv of cumulative radiation dose over five years across 27 countries, mostly in Europe. 

  • Radiation at such levels is concerning due to the established dose-response nature of current radiation theory – that is, higher doses are believed to lead to higher cancer risk.

Radiation dose exposure rates for CT, fluoroscopy-guided interventions, and PET were analyzed for 2022 for 513M people from Austria to the U.K., with a particular focus on patients getting over 100 mSv in a five-year period. 

  • For point of reference, a chest X-ray PA view is typically 0.02 mSv, a CT scan 1-10 mSv, and the average for a year of background radiation is about 3 mSv.

Researchers found … 

  • In all, 0.27% of the population received cumulative radiation exposure over 100 mSv.
  • The countries with the highest rates of patients per 1k getting over 100 mSv included Belgium (4.52), Portugal (4.48), Luxembourg (4.19), and France (4.15).
  • These same countries also tended to have the highest use rates of CT exams per 1k population, led by Portugal (285), Luxembourg (249), Belgium (226), and France (224).
  • Countries with the lowest exposure rates over 100 mSv included Finland (1.09), Romania (1.1), Norway (1.64), and Bulgaria (1.76), and all had CT use rates below 100 exams per 1k population.

While the U.S. was not included in the study, other research shows it might fall at the upper end of the scale – if not at the top. 

The Takeaway

The new study offers a sobering take on the radiation dose issue. While reasonable people can debate the exact link between low-level radiation exposure and cancer risk, it’s harder to justify such wide variation in CT use and cumulative radiation exposure between countries, especially those at similar levels of economic development.

Reporting Rules at SIIM 2025

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine offered a great opportunity to take stock of the imaging IT segment. At SIIM 2025, radiology reporting solutions – many powered by AI – were among the most exciting technologies under discussion at Portland’s Oregon Convention Center. 

As we mentioned in our video highlights roundup, attendance seemed a bit lighter at SIIM 2025, perhaps due to the Portland location and timing before a holiday weekend. 

  • But the number of vendors exhibiting at SIIM 2025 cracked 100 for the first time in years, underscoring the meeting’s importance as well as the overall growth of the imaging IT segment as the rise of AI spurs startup creation.

Every SIIM conference provides a fascinating early look at the trends and technologies that will shape radiology’s future, and this year’s meeting was no exception … 

  • Radiology Reporting Rules. The report is the radiologist’s final product, and SIIM 2025 presentations highlighted how important it is to improve this process, especially with AI. An entire track on May 21 was devoted to AI-enhanced reporting solutions, and on the exhibit floor companies showed AI-enhanced solutions that interpret radiologist findings and create structured reports from them. 
  • Questions about AI Adoption. As with past SIIM conferences, questions persist about the pace of AI adoption as well as the FDA’s regulatory direction since the Trump Administration took over. In SIIM 2025’s keynote address, health policy expert Rohini Kosoglu urged SIIM and the radiology community to take a more active role in self-regulation of AI in the absence of stronger direction from the federal government. 
  • Cloud Adoption Gains Steam. There are no such doubts about cloud-based image management, as providers are getting over past concerns about the technology. One enterprise image management vendor told The Imaging Wire that 100% of their new system orders included some form of cloud component. On the other hand, imaging IT expert Herman Oosterwijk sees some imaging sites having “second thoughts” about cloud hosting. 

The Takeaway

The growing prominence of radiology reporting software at SIIM 2025 illustrates the heightened interest in imaging IT solutions that enhance radiologist productivity rather than assist them with interpreting images – a job many feel they can do well enough on their own. 

SIIM 2025 Video Highlights

Posted on by Brian Casey

The annual meeting of the Society for Imaging Informatics in Medicine convened in Portland, Oregon, with members of radiology’s imaging IT community joining together to discuss the latest trends in enterprise imaging, AI, and more. 

As with other recent radiology meetings, AI dominated the discussion at SIIM 2025. But AI’s potential to revolutionize radiology has been tempered by nagging concerns about slow clinical adoption and questionable return on investment for healthcare providers.

Regulatory turbulence is also a concern, highlighted by recent changes implemented by the Trump Administration at the FDA. Some industry observers have speculated that AI approvals have slowed down, while others point out that the FDA – which has lagged other countries in approving new AI algorithms – perhaps might benefit from a fresh approach in how it regulates AI.

The Takeaway 

In the end, SIIM 2025 can be chalked up as another success for the organization. While attendance seemed to be down slightly (most likely due to the West Coast location and pre-Memorial Day timing), the society pointed out that the number of vendor exhibitors at SIIM 2025 exceeded 100 for the first time in years – a sure sign of a healthy imaging IT industry. 

Check out our SIIM 2025 videos below or visit the Shows page on our website, as well as our YouTube and LinkedIn pages, and keep an eye out for our next Imaging Wire newsletter on Thursday.

Keeping Pace with Volume: 7 Strategies from ASNR 2025

Posted on by T. Campbell Arnold

This week weary neuroradiologists descended upon the City of Brotherly Love for the annual meeting of the American Society of Neuroradiology (ASNR). The field is facing mounting pressure as increasing imaging volumes continue to outstrip radiologists’ capacity. 

Dealing with growing volume was a recurring theme throughout ASNR 2025, with a range of proposed solutions, including the seven strategies below:

  1. Acquisition automation for higher efficiency and reduced technical requirement: A talk by Lawrence Tanenbaum, MD, featured a number of AI solutions to ease technologist training requirements, including smart protocoling, automated patient positioning, one-touch exams without parameter adjustments, and on-device quality assurance and motion correction to cut down repeat exams.
  2. Accelerated acquisitions as the standard-of-care: Every manufacturer – from established vendors to emerging startups – showcased deep learning-based reconstruction. As Suzie Bash, MD, put it, “Deep learning reconstruction is becoming standard-of-care across the industry.”
  3. Improving radiologist reading efficiency with AI and workflow management: A noticeable trend at ASNR 2025 was fewer talks focused solely on algorithm accuracy and more emphasis on how AI impacts reading efficiency. Accuracy remains critical, but adoption increasingly hinges on demonstrating workflow efficiency.
  4. Streamlining new algorithm rollout using integrated platforms: In a session on AI adoption and evaluation, Reza Forghani, MD, PhD, called for increased use of integrated platforms to allow for easier algorithm deployment, validation, and monitoring.
  5. Rising reliance on international medical graduates (IMGs): Mina Hesami, MD, presented on the rising contribution of IMGs to US radiology, noting a steady increase in the proportion of residency slots, fellowships, and leadership roles held by international graduates – with radiology seeing faster growth than most other medical specialties.
  6. Expanding the radiology workforce with mid-level providers: Another proposed strategy is offloading specific tasks to mid-level providers. While still controversial in radiology, this model is gaining traction in response to workforce shortages.
  7. Sustainability by reducing emissions and environmental impact: Several ASNR sessions addressed environmental sustainability. From simply turning off idle scanners to using AI to reduce contrast doses, radiologists are beginning to reckon with the environmental impact of rising scan volumes.

The Takeaway

The sessions at ASNR 2025 indicate that while there’s a lot of buzz around AI, radiologists are considering every tool at their disposal to keep up with rising imaging volumes. AI will play a role, but likely won’t be sufficient alone to keep up with increasing volumes.

T. Campbell Arnold is a research scientist at Subtle Medical and the managing editor of RadAccess.

Imaging Workload Jumps with Higher Use of CT, MRI

Posted on by Brian Casey

Radiology’s shift to more advanced modalities like CT and MRI is increasing the burden on radiologists to interpret more complex exams. A new study in JACR documents the trend, finding that radiologist workload for inpatient imaging has risen sharply over the last 10 years. 

Like many physicians, radiologists are feeling burned out from rising patient workload, personnel shortages, and declining reimbursement. 

  • But radiology has the added burden of being one of healthcare’s most technology-focused specialties, with new imaging modalities giving them cooler tools to work with, but at the cost of steadily increasing exam complexity.

Researchers from Brigham and Women’s Hospital have been tracking inpatient imaging utilization for the past 40 years, and the new paper provides the latest update. 

  • They calculated inpatient imaging volume at Brigham and Women’s from 2012 to 2023, during which 896k imaging exams were performed.  

Results for the study were as follows …

  • Total annual inpatient imaging volume grew 17% over 10 years (102k to 119k exams).
  • Total imaging exams per patient admission (adjusted by case mix and disease severity) fell 20% due to declines in X-ray, ultrasound, and nuclear medicine.
  • But imaging exams per patient admission grew for CT (19%) and MRI (21%).
  • Leading to growth in CT and MRI’s combined share of all radiology global RVUs (62% to 75%).
  • Hospital length of stay rose 32% (5.6 to 7.4 days), possibly due to the COVID-19 pandemic. 

What does it all mean? Basically, the number of inpatient imaging exams per patient is declining when adjusted for disease severity, but radiologists are still having to work harder because the studies are more complex. 

  • Imaging could also be shifting from the inpatient setting to outpatient centers due to reimbursement changes aimed at shifting exams to lower-cost settings than hospitals.

One big question with the new study is the degree to which the COVID-19 pandemic skewed the results compared with previous years. 

  • The pandemic may have spurred more use of CT, especially given its value in providing a definitive diagnosis of SARS-CoV-2 infection. 

The Takeaway

If you feel like you’re working harder than ever, the new findings show that you’re not crazy. And given radiology’s breakneck pace of innovation, it’s not likely the trends revealed in the new study will let up any time soon.

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