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AI Spots Lung Nodules, Quiet Quitting, and Aneurysm Imaging
September 18, 2025
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“Once thought immune to the disengagement and disillusionment seen in more transactional industries, academic medicine now finds itself grappling with a silent exodus – not of bodies, but of spirits.”
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Subha Ghosh, MD, in an AJR article on quiet quitting in academic radiology.
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A new study in Radiology on an AI algorithm for analyzing lung nodules on CT lung cancer screening exams shows that radiologists may be able to have their cake and eat it too: better identification of malignant nodules with lower false-positive rates.
The rising utilization of low-dose CT screening is great news for clinicians (and eligible patients), but managing suspicious nodules remains a major challenge, as false-positive findings expose patients to unnecessary biopsies and costs.
- False-positive rates have come down somewhat from the high rates seen in the big lung cancer screening clinical trials like NLST and NELSON, but there is still room for improvement.
Dutch researchers applied AI to the problem, developing a deep learning algorithm trained on 16.1k NLST nodules that produces a score from 0% to 100% based on a nodule’s likelihood of malignancy.
- They then tested the algorithm with baseline screening rounds of 4.1k patients from three datasets drawn from different lung cancer screening trials: NELSON, DLSCT in Denmark, and MILD in Italy.
The algorithm’s performance was compared to the Pan-Canadian Early Detection of Lung Cancer model, a widely used clinical guideline that uses patient characteristics like age and family history and nodule characteristics size and location to estimate risk.
Compared to PanCan, the deep learning algorithm…
- Reduced false-positive findings sharply by classifying more benign cases as low risk (68% vs. 47%) when set at 100% sensitivity for cancers diagnosed within one year.
- For all nodules, achieved comparable AUCs at one year (0.98 vs. 0.98), two years (0.96 vs. 0.94), and throughout screening (0.94 vs. 0.93).
- For indeterminate nodules 5-15 mm, significantly outperformed PanCan at one year (0.95 vs. 0.91), two years (0.94 vs. 0.88), and throughout screening (0.91 vs. 0.86).
The model’s performance for indeterminate nodules is particularly intriguing, as these are challenging to manage due to their small size and can lead to unnecessary follow-up procedures.
The Takeaway
Using AI to differentiate malignant from benign nodules promises to make CT lung cancer screening more accurate and easier to perform than manual nodule classification methods – and should add to the exam’s growing momentum.
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Opportunistic Detection of CAC and Pulmonary Nodules
Achieve a newfound certainty of search for thoracic CT when using ClearRead CT from Riverain Technologies. It’s a natural addition for opportunistic CAC scoring and nodule detection, or as part of a CT lung cancer screening program.
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Ahead in the Cloud
What do healthcare providers need to consider as they adopt cloud-based solutions for medical imaging? Read this article written for Mach7 Technologies by Eliot Siegel, MD, to learn the important role cloud-based technologies are having in shaping the future of healthcare.
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- ‘Quiet Quitting’ in Academic Radiology: Academic radiology is seeing an epidemic of “quiet quitting” – in which radiologists do the bare minimum required of a job rather than seek professional enrichment. That’s according to an editorial in AJR that explains the trend, which author Subha Ghosh, MD, of Cleveland Clinic believes is caused by “inequitable workloads, opaque advancement structures, and eroding institutional support,” particularly among mid-career radiologists. Fortunately, Ghosh offers suggestions to slow the trend, from more transparent promotion pathways to protected vacation and academic time.
- Medicare’s AI Rejections: Landing reimbursements is a primary goal for many AI startups, but a JACR study showed that most radiology AI Medicare claims filed between 2018–2023 were rejected. Across 83k AI claims for 23 different services, Medicare approved 47% of claims worth $8M, while denying 53% worth $16.4M. The better news for AI insiders is that the number of AI claims (1,507 to 53,586) and acceptance rate of AI claims (28.1% to 31.8%) both increased during the study.
- Aneurysm Imaging Cuts Mortality: Using medical imaging to follow patients with thoracic aortic aneurysm cut mortality in a new study of 13k Medicare beneficiaries. People with a diagnosis of TAA without rupture were followed for two years, and patients who got ≥ 3 images as part of their surveillance had lower mortality rates compared to those with no imaging (16% vs. 13%), although they also had far more frequent surgical interventions (6.6% vs. 0.4%) and higher all-cause hospitalization (76% vs. 42%). CT was the most commonly used modality (41%).
- Genetesis MCG Acquired: “After a decade committed to biomagnetism,” Genetesis announced the sale of its key magnetocardiography assets to a strategic partner, who will continue work towards making MCG a go-to cardiac imaging modality. The CardioFlux Magnetocardiograph system measures the heart’s magnetic fields to quickly detect ischemic heart disease, and has achieved a pair of Breakthrough Designations for myocardial ischemia detection.
- CoRaX AI Fixes Perceptual Errors: In the debate over how radiologists should best use AI, a new paper in Radiology: Artificial Intelligence offers a fascinating perspective. Researchers developed a personalized AI algorithm called CoRaX that integrates radiology reports and eye gaze data to reduce radiologist perceptual errors. They tested it by introducing errors in 28% of abnormalities in a dataset of 332 chest X-ray images, finding CoRaX fixed 21% of images, leaving just 6.6% unresolved. CoRaX could act as an expert supervisor for radiologists.
- Canon CVUS by Guardsman: Guardsman Scientific is now the exclusive U.S. distributor of Canon’s cardiovascular ultrasound lineup, taking over for Canon’s direct operations. As part of the deal, Guardsman will integrate Canon’s CVUS team while taking over Canon’s CVUS installed base. The move comes amid significant competition from major ultrasound makers like GE HealthCare, Siemens Healthineers, and Philips.
- Ultrasound AI Touts Study, Gets Patent: Colorado-based startup Ultrasound AI is highlighting results of a research study underscoring its obstetric AI algorithm’s ability to predict time to delivery from ultrasound images. In the PAIR study, the company’s AI was used to predict days to delivery in 1.2k women, with high accuracy for predicting delivery time for both term and all births (R2 = 0.95 and 0.92). Separately, Ultrasound AI touted a U.S. patent it received for determining current and future lab values from noninvasive images like ultrasound.
- Prior Authorization Harms: A systematic review of 25 studies evaluating the consequences of prior authorization reached an unsurprising conclusion: Prior auth delays are associated with measurable patient harm. The investigation in The American Journal of Medicine found that patients with common conditions (asthma, IBD, AFib, epilepsy) who were subject to prior auth delays frequently experience avoidable complications resulting in ED visits, hospitalizations, or invasive procedures. The authors stress that payors’ ability to override clinical decision-making allows them to “essentially control the quality” of a large portion of U.S. healthcare.
- GE Boosts Flyrcado Distribution: GE HealthCare boosted distribution of its Flyrcado cardiac PET radiotracer through an agreement with CardioNavix, part of CDL Nuclear Technologies. CDL and CardioNavix have 225 U.S. customers and support 220k cardiac PET procedures annually, so the agreement should enhance Flyrcado’s penetration of private cardiology practices as well as office and hospital settings. GE is positioning PET with Flyrcado as an alternative to SPECT-based myocardial perfusion imaging exams.
- Digital Tomo for Lung Cancer Screening: Digital tomosynthesis has struggled to find a role in chest radiography, but a WCLC 2025 study suggests it could be an alternative to CT-based lung cancer screening in resource-constrained areas. Researchers in Thailand used digital tomo in 634 patients who got baseline low-dose CT scans and then follow-up with digital tomo every other year. Their protocol saw 50% of cancers detected at stage I, comparable to rates in randomized controlled trials that only used CT.
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Leading the Way in AI Transparency
There’s a need to better inform radiologists about AI’s role when interpreting images and generating measurements. Visage Imaging’s Visage 7 can display text in the viewer indicating that AI was used as a diagnostic aid – find out how it works today.
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How AI Is Redefining Data Migration
Enlitic’s Migratek data migration services – combined with AI-enabled ENDEX data standardization – is changing the game for data migration projects. Discover how it can benefit you in this article.
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- Bringing Real-Time AI Echo to Clinicians: Us2.ai and deepc are bringing real-time standardized AI echo analysis and reporting to clinicians through their new partnership. Find out how Us2.ai’s software is now available within the deepcOS platform.
- Cutting-Edge PET/CT to Support Theranostics: Theranostics is an exciting new field that combines diagnostics and therapy. Discover how Florida Theranostics is using United Imaging’s uMI Panorama PET/CT scanner to establish a high-quality level of patient care.
- Perspectives on Cybersecurity in Medical Imaging: Healthcare organizations are a prime target for cyberattacks, so how can you proactively defend yourself against these threats? Learn how to protect your operations – and your patients – by watching this on-demand webinar from Merge.
- The Benefits of Structured Reporting: Kailo Medical hopes to revolutionize radiology with its structured reporting solutions. At SIIM 2025, we talked to Lauren Therriault and Denholm Rhys about the latest developments at the company and why structured reporting is a benefit to radiologists.
- Redefining Breast Imaging in the Enterprise Era: As breast imaging grows more complex, radiology teams need more than siloed tools. Watch this on-demand webinar hosted by AGFA HealthCare about transforming breast imaging workflows in the enterprise imaging era.
- Revolutionize the Reading Experience: With intelligent automation and AI‑powered workflow, PowerScribe One from Microsoft allows radiologists to generate and communicate high‑quality, consistent reports – and get more done in less time.
- What’s Next for AI for Cancer Detection? AI is transforming the fight against cancer by enabling faster and more accurate cancer detection. Read this article from DeepHealth to learn how the company is pioneering new ways to advance cancer screening and broader imaging-based care.
- The Future of Fluoroscopy Is Here: The future of fluoroscopy has arrived. The LUMINOS Q.namix fluoroscopy systems from Siemens Healthineers are available on the U.S. market. Discover why they have already earned the prestigious Red Dot Design Award for intuitive design and user-centric innovation.
- Singapore’s National AI Imaging Platform: AimSG, Singapore’s national radiology AI platform, is powered by CARPL to support third-party and in-house AI through a single, secure pipeline. See how CARPL powers healthcare across Singapore.
- The Future of Radiology: In this episode of The Radiology Report, Medality’s Daniel Arnold sits down with Marc Gosselin, MD, from Vision Radiology for a thought-provoking conversation on burnout, balance, and the future of radiology.
- MRI Access and the Cost of Inpatient Stays: Longer inpatient stays due to delayed MRI access are a long-standing and costly issue for hospital systems. Find out how STAGE from SpinTech MRI can reduce your MRI backlog and inpatient stays by shortening brain scan times by 30%.
- Transform Imaging Data into Actionable Predictions: When you choose Quibim, you get more than a partner for detecting and diagnosing prostate cancer on MRI scans. Learn how they can help you transform imaging data into actionable predictions by booking a demo today.
- Cost Savings from AI for Fracture Detection: Using AI to analyze X-rays for wrist, ankle, and hip fractures in the emergency setting can save costs while improving patient care. Read how the NHS did it with Gleamer’s BoneView solution.
- Ensuring the Safety and Well-Being of Patients Undergoing MR: It’s important to create a safe environment for every patient, every scan. Unlock how thoughtful planning and innovative technology from Philips can reduce risks and enhance care during MRI procedures.
- How Intelerad Is Improving the Client Experience: Intelerad has launched a new client obsession initiative to improve the client experience with the company. We caught up with Chief Client Officer Eric Grunden to discuss the project in this episode of The Imaging Wire Show.
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