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While recently published research raised concerns about projections of future radiation-induced cancers from computed tomography (CT) scans, Joseph Cavallo, M.D., maintained in a new 

 that advances in technology may continue to facilitate lower radiation dosing with CT.

“Whether it's the photon-counting CT machines or AI post-processing algorithms, there are multiple avenues to lower dosing … It's not going to be a one size fits all, or a silver bullet. It's going to be a combination of different technologies on the machine side, on the software side, that help us continually make these examinations safer and improve the risk benefit profile moving forward,” noted Dr. Cavallo, an assistant professor of radiology and biomedical imaging at Yale School of Medicine in Boston.

Highlighting significant reductions with cardiac CT radiation dosing in recent years, Mahadevappa Mahesh, MS, Ph.D., also challenged perceptions about overuse of CT scans in the emergency room setting.

“Generally, emergency room physicians get the bad rap (with the perception that) they're ordering too many studies. … If we look (at this) closely, emergency room physicians, radiology, everybody (is) working with such a time-compressed timeline, and they're trying to save the patient. … As a medical physicist, I am all for radiation dose reduction, but at the same time, I am also a proponent of never tying anybody's hand because of radiation, even in the cath lab. If you have to do the procedure, do it,” asserted Dr. Mahesh. the president of the American Association of Physicists in Medicine (AAPM), and a professor of radiology and radiological sciences at the Johns Hopkins School of Medicine.

(Editor’s note: For related content, see “

The Reading Room Podcast: Current Insights on Recent Research About Radiation-Induced Cancers with CT Scans, Part 1

What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk

Nine Takeaways from New Research on CT Scans and Radiation-Induced Cancers

For more insights from Dr. Cavallo and Dr. Mahesh, listen below or 

In a second part of a new podcast episode on recently published research on projected radiation-induced cancers from computed tomography (CT) scans, Mahadevappa Mahesh, MS, Ph.D., and Joseph Cavallo, M.D., offer current perspectives on cardiac CT dosing, AI advances and the importance of teamwork in ensuring appropriate dosing for CT. 

The Reading Room Podcast: Current Insights on Recent Research About Radiation-Induced Cancers with CT Scans, Part 2

, Anand Narayan, M.D., and Amy Patel, M.D., offer their perspectives on the challenges faced by radiologists — who are already grappling with increasingly high volume worklists and radiologist shortages — in implementing expanded breast cancer screening as per recently updated recommendations from the 

United States Preventive Services Task Force (USPSTF)

For breast cancer risk assessments, Dr. Patel said another challenge is that not all facilities have access to high-risk breast clinics or genetics programs, limitations that often fall upon the shoulders of breast imaging centers. Dr. Patel noted that artificial intelligence (AI) and possible implementation of a risk-based model with AI may have potential for facilitating appropriate triaging of patients.

In regard to practical steps radiologists can take to help raise awareness of new recommendations to help address disparities in breast cancer screening, Dr. Patel emphasized the importance of an ongoing dialogue with primary care providers.

“Keeping the lines of communication open with your referring providers is paramount. When new recommendations come down the pike, we as a breast imaging community really need to take it upon ourselves to educate our referring providers,” noted Dr. Patel, the medical director of the Liberty Hospital Breast Care Center in Kansas City and an assistant professor at the University of Missouri-Kansas City.

New Research Shows Consequences of Delayed Diagnosis in Mammography Screening

The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 2

The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 1

For more insights from Dr. Narayan and Dr. Patel, listen below or 

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists. 

The Reading Room Podcast: Emerging Concepts in Breast Cancer Screening and Health Equity Implications, Part 3

Emerging research showed that AI-generated ADC mapping from MRI led to significant increases in accuracy, PPV and specificity in comparison to conventional ADC mapping while achieving a 93 percent sensitivity for PCa.

Study: AI-Generated ADC Maps from MRI More Than Double Specificity in Prostate Cancer Detection

Assessing the simulated use of AI-generated suspicion scores for determining whether one should continue with full MRI or shift to an abbreviated MRI, the authors of a new study noted comparable sensitivity, specificity, and positive predictive value for biopsies between the MRI approaches.

Possible Real-Time Adaptive Approach to Breast MRI Suggests ‘New Era’ of AI-Directed MRI 

The use of adjunctive AI software with pre-treatment PET imaging demonstrated over a fourfold higher likelihood of predicting progression-free survival (PFS) in patients being treated for lymphoma, according to a new meta-analysis.

Can AI Assessment of PET Imaging Predict Treatment Outcomes for Patients with Lymphoma?

Through AI recognition of subtle patterns in breast tissue on screening mammograms, the Clairity Breast software reportedly provides validated risk scoring for predicting one’s five-year risk of breast cancer.

Mammography AI Platform for Five-Year Breast Cancer Risk Prediction Gets FDA De Novo Authorization 

The Optive AI software, the tenth software release for the Swoop portable MRI system, reportedly offers enhanced clarity and anatomical detail for ultra-low-field MRI of the brain.

FDA Clears Next-Generation AI Software for Portable Brain MRI 

While some have predicted that artificial intelligence (AI) may replace radiologists, Samir Abboud, M.D., says generative AI will allow radiologists to maximize their time on image interpretation.

, Dr. Abboud and colleagues evaluated the impact of a generative AI model for providing initial drafts of radiology reports for radiographs. In a total cohort including 23,960 radiographs, the researchers found that use of the generative AI model reduced documentation time for radiology reports by a mean of 29.4 seconds.

While the study noted a 15.5 percent increase in documentation efficiency, Dr. Abboud noted in a recent interview that he has seen “truly eye-popping” efficiency gains of up to 40 to 50 percent with use of the generative AI model for initial draft reporting.

“ … Once you've decided and formed your conclusions about what's going on with that patient, you look over the report that's been generated and decide if you agree or not, and then make any corrections that are necessary. It's just a much more natural way of doing things to my mind, and I think that's the only solution you have to the workforce shortage we have in radiology,” asserted Dr. Abboud, the chief of emergency radiology at Northwestern Memorial Hospital in Chicago.

In a total of 97,651 studies, the generative AI model provided a 72.7 percent sensitivity and a 99.9 percent specificity for unexpected pneumothorax cases that were clinically significant, according to the study authors.

Dr. Abboud attributes the specificity to additional filters incorporated into the Northwestern generative AI model that provide greater context than other AI software models for differentiating between cases of expected pneumothorax and incidental detection with clinical significance. This difference is a key advantage in a large hospital system with many ICU and postoperative patients, according to Dr. Abboud.

“When we ran the trial … the number of flags I was getting a week for unexpected pneumothoraces was one or two as opposed to getting 30 or 40 flags a day,” recalled Dr. Abboud, a clinical assistant professor of radiology at the Northwestern University Feinberg School of Medicine. “You knew right off the bat that if you were getting a flag, it was a real one, not just a stack of flags to then sort through further. The clinical relevance and the net benefit to our radiologists from that sort of approach has been quite a bit more profound than using the more traditional methods.”

Generative AI Model Reduces CXR Reading Time by 42 Percent

Study: AI Bolsters Sensitivity for Pneumothorax on CXR and Significantly Reduces Reporting Time

Can Innovations with AI Help Address the Impact of Staffing Shortages on Radiology Workflows?

For more insights from Dr. Abboud, watch the video below.

In a recent interview, Samir Abboud, M.D., discussed new research examining the impact of generative AI in maximizing efficiency and reducing the time-consuming nature of radiology reporting.

Can Generative AI Reinvent Radiology Reporting?: An Interview with Samir Abboud, MD

Emphasizing the preventative benefit of computed tomography angiography (CTA), C. Alberto Morales, M.D., discussed a friend whose father had a heart attack in his mid-30s. Given the family history, the friend had annual stress testing with cardiologists and was told he was in good shape and even had a zero coronary artery calcium (CAC) score.

However, a CTA scan in Dr. Morales’s office revealed that his friend had diffuse coronary artery disease and a 70 to 80 percent blockage in the mid-right coronary artery (RCA). After aggressive management, follow-up CTA imaging at one year showed a 36 percent decrease in the RCA blockage and stabilization in other arteries, according to Dr. Morales, the owner of South Tampa Cardiology and Advanced Imaging Center in Tampa, Fla.

In a recent interview, Dr. Morales said advances in CTA scanning has led to a significant shift in preventive capabilities with CTA. In addition to significantly lower and more consistent radiation dosing (approximately three millisieverts (mSvs) per CTA scan), Dr. Morales cites the enhanced resolution going from a traditional 64-slice CT scanner to the 560-slice SpotLight CT scanner (Arineta).

“ … The resolution is so much crisper (and) the coronaries are almost still. Now you're able to identify things … such as these soft, 15-mm, 15 percent stenotic plaques that are within the wall that you are missing with the 64-slice CT. The resolution is just so much sharper and, as a result, now you're able to add other pieces of the puzzle, such as artificial intelligence (AI),” noted Dr. Morales.

Dr. Morales said the emergence of adjunctive AI tools with CTA has provided sharper analysis of plaque volume and characteristics.

“AI is leading the way with these advanced CT scanners in a completely new approach to patient care where we can actually identify disease, such as in a screening manner, treat patients, and then we can perform serial surveillance imaging to ensure that treatment is following the path of stabilization or, even in some cases, regression,” posited Dr. Morales.

Can Cardiac CTA be a Viable Alternative for Post-Op Assessment of Left Atrial Appendage (LAA) Closure?

Can Emerging AI Software Offer Detection of CAD on CCTA on Par with Radiologists?

Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque

For more insights from Dr. Morales, watch the video below.

In a recent interview, C. Alberto Morales, M.D., discussed the impact of advances with computed tomography angiography and AI, and a shifting emphasis in cardiac risk stratification from stenosis to plaque burden.

Combining Advances in Computed Tomography Angiography with AI to Enhance Preventive Care

In a recent interview, C. Alberto Morales, M.D., discussed the impact of advances with CTA imaging and AI, and a shifting emphasis in cardiac risk stratification from stenosis to plaque burden.

Magnetic resonance imaging (MRI) and prostate-specific membrane-antigen positron emission tomography (PSMA PET) both have limitations with respect to the detection of seminal vesicle invasion (SVI) in prostate cancer, noted Wayne Brisbane, M.D., in a recent interview.

“One of the issues is (MRI) is not super sensitive so if there's a small amount of seminal vesicle invasion, it might not be picked up, especially if the tumor volume is very small. But it's still important because we would risk adjust those patients and change their treatment paradigms,” noted Dr. Brisbane, an assistant professor of urology at the University of California-Los Angeles (UCLA). “ … With PSMA, there (are) other challenges. While molecular imaging is very easy to interpret, there is less spatial resolution between the prostate and the seminal vesicle so interpreting seminal vesicle invasion with PSMA and MRI can be challenging.”

However, in a recent lecture at the American Urological Association (AUA) Annual Meeting, Dr. Brisbane discussed key findings from a new study examining the use of the MRI-based artificial intelligence (AI) software Unfold AI (Avenda Health) for detecting SVI in patients with 

In an initial cohort of 147 men who underwent radical prostatectomy (with a 17 percent incidence of SVI), the researchers found that the AI software provided 92 percent sensitivity, 89 percent specificity and a 95 percent area under the receiver operating characteristic curve (AUC), 15 percent higher than the AUC for MRI alone (80 percent).

“(This) AI (software) was initially trained on (prostate) tumor volume but has found really impressive accuracy for predicting cancers coming out of the prostate or into the seminal vesicles,” noted Dr. Brisbane.

Study: PSMA PET/CT More Advantageous than MRI for Locoregional Staging of Prostate Cancer

Researchers Unveil PI-QUAL v2 for Prostate MRI Quality Assessments

Study Shows PET MRI Offers Better Detection of Localized Prostate Cancer than mpMRI

For more insights from Dr. Brisbane, watch the video below.

Wayne Brisbane, MD, discusses new research, presented at the American Urological Association (AUA) conference, which showed the benefits of the MRI-based Unfold AI software in detecting seminal vesicle invasion (SVI) in prostate cancer.

Study: MRI-Based AI Enhances Detection of Seminal Vesicle Invasion in Prostate Cancer

1. Brisbane W, Priester A, Mota SM, et al. Prediction of seminal vesicle invasion using artificial intelligence prostate cancer risk mapping. 

https://doi.org/10.1097/01.JU.0001109868.09667.98.20

 . Presented at the American Urological Association (AUA) Annual Meeting April 26-29, 2025, Las Vegas.

In a recent interview, Wayne Brisbane, M.D., discussed new research, presented at the American Urological Association (AUA) conference, which revealed a 15 percent higher AUC for an emerging AI software in detecting seminal vesicle invasion (SVI) in comparison to prostate MRI alone.

While there has been a fair amount of research examining the impact of artificial intelligence (AI) with two-dimensional mammography, there has been relatively limited data on the use of AI with digital breast tomosynthesis (DBT), noted Manisha Bahl, M.D., MPH, FSBI, in a recent interview.

Dr. Bahl also noted there has been little data in the AI computer-assisted diagnosis (CAD) breast imaging research with respect to what specific breast cancers are detected or not detected with AI.

“The purpose of our (new) study was to fill that gap in knowledge or understanding by providing an analysis of the types of breast cancers that are missed versus detected by a commercial AI based CAD algorithm for tomosynthesis,” explained Dr. Bahl, an associate professor of radiology at Harvard Medical School.

For the study, which was recently presented at the Society for Breast Imaging (SBI) conference, Dr. Bahl and colleagues assessed the Genius AI Detection 2.0 software (Hologic) in 5,000 patients who had DBT screening exams. They found that the AI software detected and correctly localized 90 percent of true-positive cases.

Dr. Bahl noted the AI software was more likely to detect invasive ductal carcinoma and lymph node positive carcinoma. The AI software was less likely to detect true positive cancers that presented as asymmetries on mammography as well as invasive lobular carcinoma, according to Dr. Bahl.

“These results suggest that the AI algorithm is optimized to detect clinically significant lymph node positive cancers (and) may require further training to identify invasive lobular cancers, which are known to be difficult to detect because of their unique infiltrative growth pattern,” posited Dr. Bahl, the director of the breast imaging fellowship program at Massachusetts General Hospital.

Dr. Bahl also said she was impressed by the ability of the AI software to detect 32 percent of the false negative cases in the study.

“I think those results suggest that integrating AI into our practice could potentially help decrease the false negative rate of screening tomosynthesis, which is an important metric, an important surrogate marker for long term outcomes. Those results are very promising to me and show that AI has the potential to improve our quality in addition to our efficiency,” noted Dr. Bahl.

Multicenter Mammography Study Shows Greater than 10 Percent Increase in Breast Cancer Detection with Adjunctive AI

New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection

Can AI Bolster Breast Cancer Detection in DBT Screening?

For more insights from Dr. Bahl, watch the video below.

In a recent interview, Manisha Bahl, MD, discussed new research looking at the impact of AI in digital breast tomosynthesis (DBT) screening for breast cancer.

What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD

In a recent interview, Manisha Bahl, M.D., discussed key findings from a new study on AI and digital breast tomosynthesis that she presented at the Society for Breast Imaging (SBI) conference.

There is significant variability with respect to predicting postoperative recurrence risk in pediatric patients who undergo surgery for gliomas, which contributes to considerable burden and stress for patients and their families. With this in mind, Benjamin Kann, M.D., and his colleagues developed a temporal deep learning model that evaluates longitudinal magnetic resonance imaging (MRI) to ascertain the risk of pediatric glioma recurrence.

In a new study, published earlier today in 

, Dr. Kann and his colleagues found that the model’s average area under the receiver operating characteristic curve (AUROC) for predicting one-year event-free survival (EFS) with longitudinal MRI assessment was 80.6 percent in contrast to 50.3 percent for single MRI scan assessment.

In a recent interview, Dr. Kann said the temporal deep learning model may provide a viable option for synthesizing current and previous imaging at the point of care.

“The point is to really take that historical imaging and analyze it all together to provide the most accurate prediction of… what will happen within the next year for this patient,” added Dr. Kann, an assistant professor of radiation oncology at Harvard Medical School.

While emphasizing that subsequent prospective research will be necessary to validate the study findings, Dr. Kann noted the potential of the temporal deep learning model in facilitating earlier treatment interventions for patients deemed to have high recurrence risk and perhaps longer intervals between follow-up MRI scans for patients identified as having a lower risk of glioma recurrence.

Dr. Kann also suggested broader implications with the study perhaps serving as a springboard to more investigations with AI assessment of longitudinal imaging versus single scans.

“I think there's been a lot done at single time point analyzes, and for a long time, it's been seen as kind of unreachable or infeasible to do these types of longitudinal medical imaging analyzes. … We hope that we've shown that there is a path forward there, and (we’re) just excited to see other groups do even more interesting and innovative things inspired by our work,” noted Dr. Kann, a radiation oncologist affiliated with the Brigham and Women’s Hospital and the Dana-Farber Cancer Institute in Boston.

Emerging PET Agent Gets FDA Fast Track Designation for Glioma Imaging

A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas

Is MRI Contrast Enhancement Necessary for Long-Term Monitoring of Diffuse Glioma?

For more insights from Dr. Kann, watch the video below.

In a recent interview, Benjamin Kann, MD, discusses the use of AI evaluation of longitudinal MRI scans to improve the prediction of postoperative glioma recurrence in pediatric patients.

Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?

In a recent interview, Benjamin Kann, M.D., discussed the use of an emerging AI model that can assess longitudinal MR imaging to help predict postoperative recurrence risk for gliomas in pediatric patients.

The quantitative assessments provided by image-based artificial intelligence (AI) models in mammography screening may have a significant impact in ascertaining short-term breast cancer risk, according to new research presented at the European Congress of Radiology (ECR) conference.

In a study involving over 8,000 women (including 780 women with incident breast cancer), researchers found that AI-based risk models (ProFound AI Risk, iCAD) for digital mammography and digital breast tomosynthesis (DBT) predicted 40 percent and 48 percent, respectively, of future breast cancers in high-risk women.

“Another important thing is this (research was focused on) short-term risk, so this is really important to guide the choice of precision screening for women. Who needs what? How often? … How can we really use this information, this risk based on the image qualities, to drive precision screening better than we're doing it right now?,” posited lead study author Emily Conant, M.D., a professor emeritus in the Department of Radiology at the Hospital of the University of Pennsylvania.

In a recent interview, Dr. Conant also discussed another study presented at the ECR conference that examined the use of an algorithm to facilitate detection of breast arterial calcification (BAC) in mammograms. Dr. Conant and her colleagues found that the algorithm achieved a 98 percent case-level AUC for BAC detection.

“I think the fact that the mammogram shows more than just, you know, breast cancer and risk for future breast cancer, short-term risk, long-term risk, but can also help sort of triage women who are at higher cardiac risk. That's really huge,” maintained Dr. Conant. “Again, we need more studies, we need more data, we need prospective studies and all of that. But this is a rapidly expanding area that I think really adds benefit and worth, etc, to mammographic screening.”

Study: Mammography AI Leads to 29 Percent Increase in Breast Cancer Detection

Mammography Study Shows Merits of AI for Improving Breast Cancer Detection and Effectiveness of Recalls

Study Shows Link Between Breast Arterial Calcification and Significant Cardiovascular Risks

For more insights from Dr. Conant, watch the video below.

Emily Conant, MD, discusses new research, presented at the European Congress of Radiology (ECR) conference, that explores the use of image-based AI risk models and breast arterial calcification detection in mammography screening.

New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection 

In a recent interview, Emily Conant, M.D., discussed findings from two studies presented at the European Congress of Radiology (ECR) conference that showed the potential impact of image-based AI risk models and breast arterial calcification detection in mammography screening.

While single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is well validated for the detection of coronary artery disease (CAD) and other cardiovascular abnormalities, there is a degrading effect with attenuation that requires an additional computed tomography (CT) scan to address attenuation compensation.

However, in a recent interview, Abhinav Kumar Jha, Ph.D, noted there are a large number of SPECT scanners that do not have the CT component and this issue is particularly prevalent in community hospitals and rural facilities. Dr. Jha says this problem is compounded as studies have demonstrated a higher prevalence of heart disease in rural health-care settings.

Dr. Jha pointed out that 75 percent of SPECT MPI exams are performed without attenuation compensation and cites a survey of nuclear cardiology providers that found only 5.6 percent provided CT-based attenuation correction.

“Therefore, you can imagine there's a very important need for a large segment of the patient population to have access to this technology of attenuation correction without being able to take a CT scan,” noted Dr. Jha, an associate professor of biomedical engineering and radiology at the Washington University School of Medicine in St. Louis.

However, in recently published research, Dr. Jha and other researchers developed a deep learning technique that provides CT-less (dubbed CTLESS) attenuation compensation. With this research and other studies, Dr. Jha said the CTLESS approach has provided consistent performance across scanner models and patients with different degrees of heart disease.

While acknowledging that more studies are needed to validate the technology, Dr. Jha noted the CTLESS approach has potential to bolster access to SPECT MPI.

“I'm excited about the CTLESS technology (and) making the process of attenuation compensation more accessible, affordable as well as safe,” emphasized Dr. Jha.

Assessing MACE Risk in Women: Can an Emerging Model with SPECT MPI Imaging Have an Impact?

Radiology ‘Game-Changer’: FDA Approves PET Agent for Enhanced Detection of Coronary Artery Disease

Study Shows Merits of CTA-Derived Quantitative Flow Ratio in Predicting MACE

For more insights from Dr. Jha, watch the video below.

Abhinav K. Jha, Ph.D., discusses emerging research for a deep learning CT-less alternative for providing attenuation compensation for SPECT MPI.

Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?

In a recent interview, Abhinav K. Jha, Ph.D., discussed key challenges with the use of SPECT MRI and how an emerging deep learning model may facilitate attenuation compensation without the need for an additional computed tomography (CT) scan.

For busy neuroradiology departments, adjunctive artificial intelligence (AI) may facilitate timely detection and triage of computed tomography (CT) scans showing subdural hematomas (SDHs).

In a new retrospective multicenter study presented at the International Stroke Conference (ISC), researchers compared the AI-powered Rapid SDH (RapidAI) software versus three expert neuroradiologist assessments of non-contrast-enhanced head CTs in 313 patients (mean age of 63) for the detection of SDH.

The researchers found that the AI software provided a 92.4 percent sensitivity and a 98.7 percent specificity for subdural hematomas. Specifically, the Rapid SDH software offered a 91.5 percent sensitivity for chronic SDH and a 92.7 percent sensitivity for acute/subacute SDH, according to the research.

In a recent interview from the ISC, lead study author Jeremy Heit, M.D., Ph.D., spoke about the potential impact of the research.

“Those are really good numbers. This is a hard problem, and as one of the readers, some of these hemorrhages were really subtle,” noted Dr. Heit, the chief of neuroimaging and neurointervention at the Stanford University School of Medicine. “So the fact that they're being picked up, I actually think that's really impressive because it's not a trivial task for a computer to be able to deal with.”

Bringing up an example of multiple traumas requiring a number of acute imaging scans in an emergency department, Dr. Heit said the accuracy and efficiency of the Rapid SDH software can have a significant impact in triaging important cases.

In a video interview from the International Stroke Conference (ISC), Jeremy Heit, M.D., Ph.D., discussed new research revealing over 90 percent sensitivity and specificity rates for AI detection of subdural hematomas on non-contrast-enhanced head CTs.

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