Technology

Neurology CT

Cardiac CT

Women's Health CT

Oncology CT

Facility Management

Neurology MRI

Oncology MRI

Women's Health MRI

Cardiac MRI

Mammography

Molecular Imaging

Women's Health Ultrasound

Oncology Ultrasound

Cardiac Ultrasound

Ultrasound

Orthopedic X-Ray

X-Ray

News

All News

Blogs

Media

Around the Practice 

Between the Lines

Image IQ

Medical World News

Podcasts

Rad-View

Videos

Conferences

Conference Coverage

Conference Listing

DI Executive

Resources

CME/CE

E-books

Education

Interactive Tool Kits

Partners

Subscribe

Advertise

About

Contact Us

Editorial

Editorial Board

Do Not Sell My Personal Information 

Privacy

Terms and Conditions

Diagnostic Imaging

Facebook

LinkedIn

YouTube

Spotlight

Disease Spotlight: Prostate Cancer

Image IQ Quiz

RSNA 2023

Rad View: PSMA PET Imaging Recommendations

Rad-View: Generic Contrast Agents 

Unlock AI-Powered Imaging

Topics

View More

1rem

Topic

Slideshows

Catch up on the top radiology content of the past week.

Diagnostic Imaging's Weekly Scan: September 4-September 10

Articles

Researchers found that stand-alone use of an artificial intelligence (AI) model led to a 24.9 percent increase in sensitivity for diagnosing pulmonary nodules and a 21.4 percent increase in sensitivity for diagnosing pneumonia.

Study Suggests AI Improves Chest X-Ray Interpretation

, 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

While GPT-4 demonstrated higher overall accuracy than other large language models in answering ACR Diagnostic in Training Exam multiple-choice questions, researchers noted an eight percent decrease in GPT-4’s accuracy rate from the first month to the third month of the study.

New Study Examines Short-Term Consistency of Large Language Models in Radiology

Emerging trends with artificial intelligence and cloud technology may reinvent efficiency and scalability with radiology workflows.

How the Convergence of AI and the Cloud Can Unlock Breakthroughs and Cost Savings in Radiology 

The AI-enabled EchoGo® Amyloidosis software for echocardiography has reportedly demonstrated an 84.5 percent sensitivity rate for diagnosing cardiac amyloidosis in heart failure patients 65 years of age and older.

FDA Clears AI-Powered Ultrasound Software for Cardiac Amyloidosis Detection

Reportedly developed on over 90,000 ultrasound exams, the BrightHeart AI software identifies suspicious findings suggestive of congenital heart defects through AI-powered evaluation of fetal heart morphology.



FDA Clears AI Software for Fetal Heart Ultrasound Assessment

Icobrain.aria is reportedly the first AI software geared toward the detection and monitoring of amyloid-related imaging abnormalities (ARIAs) on brain MRI.

FDA Clears AI-Powered MRI Software for ARIA Detection in Patients with Alzheimer’s Disease

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. 

Nina Kottler, MD, MS on Adoption and Impact of AI 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.

The Executive Order on AI: Promising Development in Radiology or 'HIPAA for AI'?

For incidental pulmonary emboli (PE) findings on outpatient computed tomography (CT) scans, timely follow-up remains challenging due to daunting radiology worklists and potential health equity considerations, such as transportation access and language barriers, in high-risk populations.

However, in emerging pilot study research presented at the Society for Imaging Informatics in Medicine (SIIM) conference, Ali Tejani, M.D. discussed the implementation of an artificial intelligence (AI)-guided triage software for managing incidental PE findings on computed tomography (CT) scans.

Dr. Tejani and colleagues had impressive results with an approximately one-hour improvement in the median time to emergency department presentation and over a 13-hour difference in the median time to anticoagulant prescription retrieval.

Emerging AI Tool Improves Worklist Triage and CT Detection of Incidental Pulmonary Embolism

Can AI Improve Triage of CT Pulmonary Angiography Exams for Acute PE?

However, Dr. Tejani emphasized that these results wouldn’t have been possible without multidisciplinary collaboration in addressing previously existing challenges in the radiology workflow, including health equity considerations, that affect the quality of care. In collaboration with CT technologists, Dr. Tejani said they established a workflow requirement that patients with outpatient chest CT studies remain at the facility during AI assessment of the imaging. 

The combination of workflow changes with AI-enabled worklist reprioritization for radiologists led to more timely follow-up care for patients with incidental PE findings on CT scans, according to Dr. Tejani, a third-year radiology resident at the University of Texas Southwestern Medical Center.

“By instituting a clinical operations initiative that helps you leverage an AI result, (it) can actually translate to this clinically meaningful change,” noted Dr. Tejani.

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

In a recent video interview from the Society for Imaging Informatics in Medicine (SIIM) conference, Ali Tejani, M.D., discussed pertinent insights on leveraging the value of adjunctive artificial intelligence (AI)-enabled triage software for computed tomography (CT) scans with radiology workflow improvements to achieve “clinically meaningful change” for patients with incidental pulmonary emboli findings.

Expediting the Management of Incidental Pulmonary Emboli on CT

The recently released ChatGPT-4 (Open AI) may offer more advanced reasoning, be less prone to hallucinations, and be more capable of passing a radiology board exam than ChatGPT-3.5 (Open AI), according to newly published research.

In prospective studies published recently in 

, researchers assessed the performance of 

 in answering 150 text-based multiple-choice questions akin to those found on a radiology board examination.

The researchers found that the ChatGPT-4 model correctly answered more than 80 percent of the questions in comparison to 69 percent for ChatGPT-3.5. ChatGPT-4 also demonstrated a greater than 20 percent improvement over ChatGPT-3.5 on questions that required higher-order thinking, including description of imaging findings, classifications, and application of concepts, according to the study authors.

In a recent interview, Rajesh Bhayana, MD, FRCPC, the lead author of the studies, said it is apparent that the technology with ChatGPT is showing significant improvement.

“The fact that ChatGPT-4 performed better than ChatGPT-3.5 and had less frequent incorrect answers and also performed better with higher-order reasoning suggests the frequency of hallucinations is in fact decreasing,” noted Dr. Bhayana, an abdominal radiologist, and technology lead in the Department of Medical Imaging at the University of Toronto in Canada.

Can ChatGPT Provide Appropriate Information on Mammography and Other Breast Cancer Screening Topics?

While Dr. Bhayana said there is significant potential with the use of ChatGPT in radiology, he cautioned that accuracy remains an issue and use of the technology still requires rigorous fact checking.

“It was very impressive that these models, based on the way they work and based on the fact that they are general models, performed so well in a specialty like radiology where language is so critical,” maintained Dr. Bhayana. “(But) it still does get things wrong. When it does get those things wrong, it uses very confident language. If you’re a novice and you can’t separate fact from fiction, it can be tough to know what’s right and what’s wrong. Especially for education, especially for novices when you’re looking up that information and learning something for the first time, you can’t rely on it. If you do use it, you have to always fact check it.”

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

In a recent interview, Rajesh Bhayana, M.D., shared insights from new research that compared the abilities of ChatGPT-3.5 and ChatGPT-4 to answer text-based questions akin to those found on a radiology board examination.

Can ChatGPT Pass a Radiology Board Exam?

When evaluating an artificial intelligence (AI) model for initial assessment of left ventricular ejection fraction (LVEF) on echocardiograms, David Ouyang, M.D., said he was genuinely surprised that cardiologists had greater confidence in initial AI assessments than those provided by cardiac sonographers with an average of 14.1 years of experience.

In the randomized trial, recently published in 

, cardiologists revised 27.2 percent of initial LVEF assessments by sonographers in comparison to 16.8 percent of AI assessments.

“We initially thought of the tool as kind of a streamlining tool where it speeds things up. We actually designed the trial as a non-inferior trial. We weren’t expecting AI to be better than sonographers. Hopefully, it would be equivalent,” noted Dr. Ouyang, who is affiliated with the Department of Cardiology at the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine at the Cedars-Sinai Medical Center in Los Angeles. “We were pleasantly surprised that AI actually was superior. This really speaks to the high level of precision that automation delivers.”

Study Finds AI More Effective Than Sonographer Interpretation of Cardiac Function on Echocardiograms

AI Platform for Ultrasound Detection of Cardiac Amyloidosis Gets FDA Breakthrough Device Designation

In a recent interview, Dr. Ouyang also discussed another 

 that employed deep learning segmentation of cardiac magnetic resonance imaging (MRI) to examine how variations in left ventricle sphericity can impact cardiovascular risks. Based on their findings from a large cohort of nearly 39,000 participants from the UK Biobank, Dr. Ouyang and colleagues found that one standard deviation increase in the left ventricle sphericity index led to a 47 percent increased risk for cardiomyopathy and a 20 percent higher risk of atrial fibrillation.

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

In a recent video interview, David Ouyang, M.D., shared insights from two recent studies he co-authored on the use of artificial intelligence (AI) to improve initial assessment of left ventricular ejection fraction (LVEF) on echocardiography and ascertain cardiac risks associated with changes in the left ventricle sphericity index seen on magnetic resonance imaging (MRI).

Emerging AI Advances in Cardiac Imaging

ChatGPT may have considerable potential for streamlining radiology workflows and reporting but concerns over optimal prompt engineering and hallucination effects may curtail the use of the artificial intelligence (AI) chatbot in radiology for the time being.

In recent video interviews. Nina Kottler, M.D., MS, Tessa Cook, M.D., Ph.D, and Sonia Gupta, M.D., discussed the potential capabilities of ChatGPT for radiologists and cautioned about current drawbacks of the technology.

Dr. Kottler suggested that ChatGPT could help remedy the frustration of receiving incomplete patient histories.

“We’ve never (received) great (clinical) histories. That decreases the quality of our output and causes us to hedge a lot,” notes Dr. Kottler, associate chief medical officer of Clinical AI and vice-president of Clinical Operations at Radiology Partners. “ChatGPT could either summarize information from the EMR (electronic medical record) and provide us with that robust patient history or summarize prior reports for us so we get that history and the pathology that we’re finding.”

Another potential benefit of ChatGPT could be converting patient summaries of radiology reports into different languages and possibly expanding access to care, suggested Dr. Gupta, the chief medical officer of Change Healthcare and a board member of the American Board of Artificial Intelligence.

Emerging Insights on Improving Radiology Workflows

Assessing the Value Proposition of AI in Radiology

Emerging Trends with AI in Radiology in 2023

However, Dr. Kottler noted that the quality of the prompt input has a strong effect on the results one gets with ChatGPT. Drs. Cook, Gupta and Kottler all emphasize having appropriate safeguards in place to ensure the accuracy and verifiable referencing of information provided by ChatGPT. While Dr. Cook noted use cases with ChatGPT that have provided a synthesis of literature on a given topic, she said other cases have demonstrated “hallucination effects” in which there has been rewritten history or the “invention of citations that don’t exist.”

“Just because ChatGPT is far more fluent than any other chatbot we might have encountered in the past doesn’t necessarily mean it is not fallible in some way as well,” cautioned Dr. Cook, an associate professor of radiology, and director of the Center for Practice Transformation in Radiology at the Hospital of the University of Pennsylvania.

For more insights from Drs. Cook, Gupta and Kottler, watch the video below.

In recent video interviews, Tessa Cook, MD, PhD, Nina Kottler, MD, MS, and Sonia Gupta, MD shared their insights and perspective on potential benefits and drawbacks of ChatGPT in radiology.

Can ChatGPT Have an Impact in Radiology?

One of the ongoing trends with the development of artificial intelligence (AI) modalities in radiology is an increasing emphasis on improving efficiencies with non-interpretative tasks.

In a recent interview, Sonia Gupta, MD said the combination of AI and natural language processing can help retrieve and analyze key historical points from a patient’s electronic medical record (EMR). Dr. Gupta emphasized that this technology could help streamline patient triage, radiology reporting and the management of incidental finding follow-up.

“(With) incidental finding follow-up, AI reads the report and detects the findings that need follow-up … and can also insert the follow-up recommendations for you into the report. Again, this is all non-interpretative and natural language processing-based type of AI. I think that is a really great opportunity we can all utilize,” maintained Dr. Gupta, an abdominal radiologist, a board member of the American Board of Artificial Intelligence in Medicine, and the chief medical officer of Change Healthcare.

AI in Radiology: Top Five Articles of 2022

Dr. Gupta said the hope is that this emphasis on simplifying non-interpretative tasks could reduce time-consuming elements of radiology workflows and help curtail burnout.

Noting that approximately one-third of radiologists are currently utilizing some form of AI in practice, Dr. Gupta said the combination of cancer screening with AI and appropriate reimbursement could accelerate AI adoption in radiology.

Pointing out there are currently upward of 200 Food and Drug Administration (FDA) 510(k) clearances in radiology, Dr. Gupta said the ongoing consolidation of AI vendors may make it easier for radiologists to choose among relevant systems for their practice. She added that consolidation could be a boon for early adopters, who may gain access to more AI offerings than they originally signed up for.

In a recent video interview, Sonia Gupta, MD discussed a number of ongoing developments with artificial intelligence (AI) in radiology, ranging from market consolidation of AI vendors to maximizing automation and efficiency with patient triage, reporting and follow-up of incidental findings.

Combining artificial intelligence detection (AI) algorithms with radiologist assessment may improve detection rates anywhere between two to 45 percent, according to Nina Kottler, M.D., M.S., the Associate Chief Medical Officer of Clinical AI and Vice-President of Clinical Operations at Radiology Partners.

However, in a recent video interview, Dr. Kottler emphasized that recognition and appropriate education on the potential biases of AI algorithms and inherent radiologist biases are key to maximizing the impact of AI on patient outcomes.

Dr. Kottler noted that factors such as the patient history, the exam type ordered, an unusual location for a lesion and distracting pathology can contribute to variability with radiologist assessment. She also pointed out that education biases stemming from one’s experience, recently missed diagnoses and satisfaction of search can also factor into image interpretation.

However, Dr. Kottler added there are also biases or limitations with the development of AI algorithms as well.

“Most AI systems are trained on one series or a couple of images within a total image set,” noted Dr. Kottler, who lectured about AI at the recent Radiological Society of North America (RSNA) conference. “If you’re looking at a chest X-ray, a lot of the chest algorithms are trained on just the frontal image. If you’re looking at a head CT for intracranial hemorrhage, most of those (AI) algorithms are trained just on the axial, soft tissue, thinnest slice series with no contrast. It's not looking at all of the other components.”

(Editor's note: For other video interviews with RSNA lecturers, click 

Dr. Kottler emphasized appropriate education that goes beyond the sensitivity rates and area under the curve (AUC) performance of AI systems.

“We spend a lot of time in our practice trying to understand the bias of the AI (model)," maintained Dr. Kottler. "Where is it overcalling things? Where is it under-calling things? We take that information to our radiologists and let them know. We just don’t give them information like (this AI system) has a 95 percent AUC, it is going to be great. No. We go to them and say (this system) may overcall intracranial hemorrhage when the patient is moving and there is a streak artifact in the exam. It’s going to miss some of the findings in X, Y and Z cases. You bring those things to the radiologists and you’re educating them on the biases of the AI. You’re making it so (radiology) and AI are stronger together.”

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

In a video interview discussing one of her recent lectures at the Radiological Society for North America (RSNA) conference, Nina Kottler, M.D., M.S., noted how the combination of artificial intelligence (AI) and radiologist experience can help mitigate bias limitations with the development of AI algorithms as well as educational biases inherent to a radiologist’s training and experience.

Recognizing and Addressing Biases with AI and Radiologists

While there are varied perspectives on what “the cloud” represents for radiologists, Eliot Siegel, M.D., sees the promise of increased efficiency and improved patient care as the field continues to grapple with increasing imaging volume and radiologist shortages.

Beyond off-site storage of medical imaging data, cloud-based resources can provide immediate access to timely PACS updates and alternative backup options, and potentially serve as a centralized hub that may help streamline access to a patient’s clinical history and past imaging from other institutions, noted Dr. Siegel during an interview at the recent 

Radiological Society of North America (RSNA) conference

“The cloud represents a central repository that could be more patient-centered rather than institution-centered. That is a major advance,” said Dr. Siegel, a professor of Diagnostic Radiology and Nuclear Medicine and vice-chair of Information Systems at the University of Maryland School of Medicine.

Dr. Siegel also noted that cloud-based image management could enhance access to a variety of artificial intelligence (AI) platforms.

“Many of the AI vendors are offering software that is really interesting and can help me become safer, more efficient, more effective, and more productive. Many of those are things I need to consume in the cloud,” noted Dr. Siegel. “The potential of being able to send my images up to a site in the cloud and then be able to have all those different AI services and other services that I want to be able to directly interface with in the cloud is extraordinarily compelling.”

In a recent interview at the Radiological Society of North America (RSNA) conference, Eliot Siegel, M.D., discussed a variety of potential benefits with cloud-based image management in radiology, ranging from enhanced data security and economies of scale to improved access to a variety of artificial intelligence (AI) solutions to increase efficiency.

Technology

Latest News

More News