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, 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.

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

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

The Exa PACS/RIS platform reportedly combines AI-enabled worklist navigation tools with advances in multiplanar functionality and 3D-generated image segmentation.

Konica Minolta Unveils New PACS/RIS Platform for Radiologists

Featuring enhanced low-dose image quality with motion-free images, the Revolution Vibe CT system reportedly facilitates improved diagnostic clarity for patients with conditions ranging from in-stent restenosis to atrial fibrillation.

GE HealthCare Debuts AI-Powered Cardiac CT Device at ACC Conference

The AI-powered AZchest CXR software reportedly offers 93.79 percent sensitivity and a 98.57 percent AUC for pneumothorax.

FDA Clears AZchest AI Software for New CXR Applications

Artificial intelligence (AI)-powered measurement capabilities provide key features with the Compact Ultrasound 5500CV device, which was unveiled at the American College of Cardiology (ACC) conference.

New AI-Enabled Portable Ultrasound May Facilitate 50 Percent Reduction in Cardiac Imaging Scan Time

In addition to a variety of tools to promote radiology workflow efficiencies, the integration of the Gravity AI tools into the PowerServer RIS platform may reduce time-consuming prior authorizations to minutes for completion.

New Collaboration Offers Promise of Automating Prior Authorizations in Radiology with AI

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.

“I think it's really going to be helpful to have it work that quickly. It's a three-minute processing (time) to be able to flag something and bump it up on your queue. That is super helpful,” maintained Dr. Heit.

FDA Clears AI-Powered Module for Detecting Hemispheric Subdural Hematomas on CT

Key Findings on Non-Contrast CT Signal Higher Complication Risks in Patients with Intracerebral Hemorrhage

FDA Clears New AI Tool for Diagnosing Subdural Hemorrhage

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

Neuroradiologist Jeremy Heit, MD, discussed new research showing the efficacy of the AI-powered software Rapid SDH for detecting subdural hematomas on non-contrast head CT scans.

Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD

1. Heit J. Performance of Rapid SDH for detection of both acute and chronic subdural hematomas. Presented at the International Stroke Conference, February 4-7, 2025, Los Angeles. For more info, visit 

https://professional.heart.org/en/meetings/international-stroke-conference

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.

Increased transparency is a cornerstone of the recent 

draft guidance from the Food and Drug Administration (FDA) on submissions for AI-enabled software

. While this will result in an increased amount of documentation on the training as well as limitations for adjunctive use of AI models, Nina Kottler, M.D., maintained in a recent interview that this well-intended effort at transparency may fall short of having an impact in daily practice.

“ … Any of this documentation that the FDA is requiring right now is not so helpful at the point of care. It is good that they're doing it. It's not a bad thing. It's a very good thing, but we need a way now to translate that information to be helpful at the point of care,” posited Dr. Kottler, the associate chief medical officer of clinical AI at Radiology Partners.

Whether it comes in the form of heat mapping or a color-coded assessment by the adjunctive AI, Dr. Kottler says the capability of the AI tool to express a degree of confidence in the evaluation can be beneficial for end-user radiologists.

Additionally, Dr. Kottler maintained that there should be clear transparency on how the adjunctive AI is making its assessment. She cautioned that most people don’t recognize that adjunctive AI doesn’t consider every single piece of input data in its interpretation.

“ … Another thing that the FDA should be requiring is to tell the end user, what are the images, or what are the components of the language, or whatever that the AI is using so radiologists know when they're looking at a study, ‘Oh, this series wasn't even evaluated by the AI.’ That is really important information,” emphasized Dr. Kottler.

How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1

Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality

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

Nina Kottler, MD, discusses the increased transparency emphasis of the recent FDA guidance on AI-enabled software and the potential impact upon radiologists.

How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 2

In the second part of a two-part interview, Nina Kottler, M.D., says the transparency emphasis of the recent FDA guidance on AI-enabled software is welcome but needs to go beyond additional documentation to clarify how adjunctive AI is making its decisions.

The recently issued draft guidance from the Food and Drug Administration (FDA) on submissions for AI-enabled software reflects more of an emphasis on the total product life cycle for AI products, according to Nina Kottler, M.D., the Associate Chief Medical Officer of Clinical AI for Radiology Partners.

Accordingly, in a recent interview, Dr. Kottler noted that a key cornerstone of the 

 is continuous monitoring of AI software, which may be challenging for smaller AI developers in radiology. She suggested that perhaps an independent group could assist with continuous monitoring of AI software.

“ … What if we had a separate group that could not only help out the AI vendors that maybe couldn't afford to do it, or don't have access to all the data, or (are) not mature enough yet to be there?” posited Dr. Kottler, the Associate Chief Medical Officer of Clinical AI for Radiology Partners.

While Dr. Kottler said the FDA guidance’s increased emphasis on transparency of the training and development of AI software is an important priority, the resulting information needs to be conveyed in a practical way for end-user radiologists.

“Now as an end user, as I'm going through my day looking at 100 cases or 200 cases, many of which are using AI, am I going to that document to look and say, ‘Is this patient and is this exam something that (the AI software) was very specifically trained on? No, that's not realistic. So how do you translate these guidelines into something that's more realistic for the end user? That's going to be the important component. That's the part that I really care about,” maintained Dr. Kottler.

Current and Emerging Insights on AI in Breast Imaging: An Interview with Mark Traill, MD, Part 3

Diagnostic Imaging’s Top Five AI Content of 2024

Nina Kottler, MD, MS on Adoption and Impact of AI in Radiology

Nina Kottler, MD discusses the recently issued FDA guidance on AI-enabled devices and the potential impact for AI developers in radiology.

How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1 

In the first part of a two-part interview, Nina Kottler, M.D. offers insights and perspective on the recently issued guidance from the FDA on AI-enabled devices and how it may impact developers in the radiology field.

While vRad’s teleradiology breast imaging service provides remote interpretation of nearly 250,000 screening and diagnostic exams annually, Arlene Sussman, M.D., says the organization emphasizes a personalized “boutique service.”

In a recent interview, Dr. Sussman said live video streaming with very large cameras connects participating radiology providers with vRad’s remote subspecialist breast radiologists, facilitating real-time communication with technologists, referrers, and their patients. Dr. Sussman added that vRad’s 11 subspecialist breast radiologists are assigned consistent coverage of certain participating sites, enabling another level of personalized care.

“For example, I cover certain sites Mondays from eight to four. (On that day), the cases come to me. I’m the only one. They get to know me. I get to know their referrers, their patients, and the technologists. I become part of their team that day and it doesn’t vary,” emphasized Dr. Sussman, the medical director and clinical chief of women’s imaging at vRad (Virtual Radiologic).

Noting that there may be upward of 1,500 cases on a worklist at any given point in time, Dr. Sussman is a strong advocate of artificial intelligence (AI) for facilitating timely triage as well as quality metrics in mammography interpretation. She is also enthusiastic about the potential of AI incorporating prior exam findings.

“ … They say diamonds are a girl's best friend. Well, they're right, but so are prior films for comparison. If you had something that has been there for four years and it's stable by definition, it is benign, and AI doesn't know that, because it only looks at the current study. But if you had AI for the prior and then AI for the current (study), imagine how powerful that could be,” posited Dr. Sussman.

Dr. Sussman added that another powerful benefit of teleradiology is reducing disparities in access to breast imaging, a key driver for vRad’s breast imaging service since its inception.

“Our first goal was to reach patients who had limited or no access to subspecialists, and there was no reason why they should be shortchanged. That's where we started, very humbly going out to small rural practices that didn't have subspecialists, and we manned them remotely, and then we never looked back,” noted Dr. Sussman.

Can an Emerging AI Software for DBT Help Reduce Disparities in Breast Cancer Screening?

Can an Emerging AI Model Enhance Early Breast Cancer Detection on MRI?

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

In a recent interview, Arlene Sussman, M.D., discussed her experience in leading vRad’s teleradiology breast imaging service and facilitating personalized care, utilizing AI and promoting health equity.

Teleradiology and Breast Imaging: Keys to Facilitating Personalized Service, Efficiency and Equity

In a recent interview, Arlene Sussman, M.D., discussed her experience in leading vRad’s teleradiology breast imaging service, how to foster personalized care in breast cancer screening, utilizing AI to help mitigate daunting worklists and improving access to subspecialty care.

For many decades, interventionalists had to rely upon two-dimensional X-ray views to guide devices through blood vessels but the emergence of Fiber Optic RealShape (FORS) technology may enhance the ease and efficiency with which complex aortic procedures are performed.

In an interview, Atul Gupta, M.D., noted the FORS technology (Philips), which provides 3D light-based imaging of blood vessels as opposed to two-dimensional X-ray imaging, has led to a 37 percent reduction in procedure time and a 56 percent reduction in radiation exposure for complex aortic procedures at one facility as per a recent study published in the 

Dr. Gupta said the recently launched LumiGuide (Philips) takes FORS technology a step further. LumiGuide offers artificial intelligence (AI)-based recognition, which facilitates efficient registration of guidewires as well as enhanced accuracy, and 3D Hub technology that enables visualization of any catheter in 3D and without radiation, even from angles that one can’t physically achieve with a standard C-arm device, noted Dr. Gupta, the chief medical officer for Image-Guided Therapy and Precision Diagnosis at Philips.

Study Examines Photon-Counting CT for CAD Detection in Patients Having TAVR Procedures

Key Takeaways from Multidisciplinary Roundtable on Intravascular Ultrasound Use for Peripheral Vascular Interventions

Emerging Concepts with ICM Reduction and Photon Counting for Coronary CT Angiography

“With FORS technology, clinicians can carry out portions of these minimally invasive interventions like aortic (procedures) without the need for X-ray. That should minimize the risks associated with that long-term radiation exposure,” emphasized Dr. Gupta, the medical director and founder of Interventional Vascular Consultants in Philadelphia.

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

1. Finnesgard EJ, Simons JP, Jones DW, et al. Initial single-center experience using Fiber Optic RealShape guidance in complex endovascular aortic repair. 

Atul Gupta, M.D. discusses the potential impact of Fiber Optic RealShape (FORS) technology to facilitate real-time 3D imaging for complex aortic procedures.

Can Fiber Optic RealShape (FORS) Technology Provide a Viable Alternative to X-Rays for Aortic Procedures?

In a recent interview, Atul Gupta, M.D. discussed the evolution and potential impact of Fiber Optic RealShape (FORS) technology, which uses light instead of radiation to facilitate real-time 3D imaging that may lead to significant efficiencies with complex aortic procedures.

Artificial intelligence (AI) is of course one of, if not the hottest topic at this year's 

, but how is all of the hype translating to clinical practice?

As exciting as new technology can be, actually using that technology requires a great deal of effort, especially for larger institutions that are faced with training dozens of radiology staff. 

User education is a key component to adoption and uptake, according to Nina Kottler, MD, MS, associate chief medical officer of clinical artificial intelligence at Radiology Partners. Dr. Kottler spoke with 

 at RSNA 2023, where she reflected on Radiology Partners hitting (and recently surpassing) an impressive milestone: utilizing clinical AI in more than 20 million patient exams. None of that would be possible without purposeful training programs to make sure that radiology end-users are comfortable with the new technology and understand the key benefits of use. 

Dr. Kottler, who is also an associate fellow at Stanford University's Center for Artificial Intelligence in Medicine & Imaging (AIMI), also spoke about what she has observed in terms of the affect that AI is having on radiology workflows. 

Watch the interview below for additional insights on AI. 

Nina Kottler, MD, MS, of Radiology Partners, discusses the importance of user education to improve uptake of AI technology and improve ROI. 

Nina Kottler, MD, MS on AI Adoption in Radiology

Click here for more coverage of RSNA 2023.

Dr. Kottler sat down with Diagnostic Imaging at RSNA 2023 to discuss AI imaging milestones and the potential impact of AI on workflows in radiology. 

While President Biden’s recently issued executive order on artificial intelligence (AI) emphasizes new standards for the development and use of AI models, Morris Panner has a few reservations and suggests that the executive order may turn out to be “HIPAA for AI” in some respects.

“As well intentioned as HIPAA (Health Insurance Portability and Accountability Act) was, it really shut off a generation of innovation in health care because of what it did to information flow and risk taking in innovation around data,” claimed Panner, the president of Intelerad Medical Systems, in a recent interview.

“I understand why we have HIPAA. We respect HIPAA. We care a lot about patient privacy. But HIPAA was and has continued to be something that most people don’t think really has lived up to its reputation as a portability act. In many ways, the executive order takes some legitimate problems and tries to address them but ends up doing it in a way that will stifle innovation and make it harder for us to take risks at the beginning of the AI innovation cycle.”

How Will President Biden’s New Executive Order on AI Impact Radiology?

Can AI Improve Triage Efficiency in Radiology Workflows for Follow-Up X-Rays?

FDA Clears Emerging AI-Enabled Teleradiology Platform

While the executive order references the bolstering of access to technical resources and assistance for smaller companies seeking to develop AI models, Panner suggests the executive order may lead to “a lot of registration and government examination” that would thwart smaller innovative companies and favor more established companies with larger regulatory capabilities but who are also more risk adverse.

For more insights from Morris Panner, watch the video below.

Morris Panner discusses President Biden's recent Executive Order on artificial intelligence(AI) and its potential impact in radiology.

The Executive Order on AI: Promising Development for Radiology or ‘HIPAA for AI’?

In a recent interview about President Biden’s recent executive order on artificial intelligence (AI), Morris Panner, the president of Intelerad Medical Systems, shared his concerns that the executive order, while well-intentioned, may wind up stifling innovation and the continued evolution of AI in radiology.

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