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Top 2026 Radiology Trends

Posted on January 7, 2026January 7, 2026 by Brian Casey

As we did in 2025, The Imaging Wire asked key opinion leaders in medical imaging to provide their predictions on the technologies, clinical applications, and regulatory developments that will shape the specialty for the next 12 months. Here’s what they said…

3 Key Radiology Trends for 2026: Three fundamental trends will drive the radiology industry in 2026: 1) AI-based workflow will become more widespread and harder to differentiate. 2) Technology and services will increasingly be bundled to drive care-pathway product solutions. 3) Intense competition will continue, with partnering and M&A growing at a faster pace as healthcare spending tightens and price pressures get worse. Steve Holloway, CEO of Signify Research

AI Consolidation Shifts to Multi-Product Platforms: Radiology AI consolidation will continue and accelerate the shift from point solutions to multi-product platforms. A few vendors will emerge as market leaders, leveraging deeper workflow integration to deliver more value, offer broad bundles at lower prices, and scale adoption. Pure intermediary platforms will compete with vertically integrated players that have become platforms. OEMs will embed more AI through partnerships or acquisitions and enter the race. Finally, in a sustained AI bull market, more startups will reach nine-figure valuations. Amine Korchi, MD, radiologist and founder of Singularity Consulting

AI Vision Language Models Impact Radiology Reporting: Use of AI vision language models for draft report generation will be a major trend. Chest X-ray models are already seeing growing real-world evaluation – VLMs for modalities like CT and MRI are the next wave. Also watch for advancements in volumetric foundation models. Winning solutions will seamlessly streamline radiologist review-and-correction workflows. The real differentiator: multimodal fusion that integrates longitudinal clinical context with imaging. Woojin Kim, MD, CSO/CMIO at HOPPR and CMO at ACR Data Science Institute

Breast Density Loopholes to Close: Dense breast reporting is now a national standard, and many states have expanded insurance coverage for breast imaging. The federal Find It Early Act would close state-specific loopholes and include federal plans. The next challenge is educating women and providers about increased or high-risk factors and guiding appropriate supplemental screening and clinical choices. JoAnn Pushkin, executive director, DenseBreast-info

Breast Imaging Moves to Risk-Based Screening: Breast imaging will continue to move toward risk-based screening, optimizing screening intervals and modalities personalized to the individual, based on risk models that combine breast density, family history, genetics, and AI-derived imaging biomarkers. AI-enhanced risk stratification will continue to gain traction, using mammographic features such as density patterns, texture, and parenchymal complexity to refine a woman’s future cancer risk. This will allow women to be triaged into personalized screening pathways: high-risk patients directed to MRI/abbreviated MRI, or intermediate-risk women to modalities such as ultrasound and contrast-enhanced mammography. Stamatia Destounis, MD, managing partner, Elizabeth Wende Breast Care

CT Lung Cancer Screening Will Save More Lives: In 2026, CT lung cancer screening will save more lives than ever before. More countries will start screening programs (e.g., Germany), and more healthcare systems will achieve the “intersection of the curves”: More early-stage than late-stage lung cancer at diagnosis. Sebastian Schmidt, head of strategy, innovation, and medical affairs for CT at Siemens Healthineers

What Will Make MRI Safer? Last year the fatal Long Island MRI accident showed us just how tenuous our safety gains are. In 2026, more conversations should take place about which specific practices would make MRI safer, and how we go about making those standard across our profession, through regulation, licensure, or accreditation. Tobias Gilk, founder of Gilk Radiology Consultants

Opportunistic Screening Adds Value: 2026 will witness a rapid expansion of published studies demonstrating the added clinical value of AI-enabled opportunistic CT screening. Furthermore, the increasing clinical availability of these automated AI tools for cardiometabolic assessment should ultimately herald their routine use in value-added patient care. Perry Pickhardt, MD, chief of gastrointestinal imaging at the University of Wisconsin

Radiation Dose Management Tools: Practices will continue to become more comfortable using dose management tools to evaluate their dose data, benchmarking their doses for a given task against their peers. Diagnostic medical physicists play a crucial role in investigating and addressing dose outliers, and their important role in dose management will continue to grow. Cynthia McCollough, PhD, professor of medical physics and biomedical engineering at Mayo Clinic

Radiologist Shortage Will Continue: Looking into 2026, the radiologist shortage remains acute. Imaging demand will continue to outpace training output, burnout will drive attrition, and AI won’t offset capacity gaps fast enough. Workforce pressure shifts from volume coverage to subspecialist scarcity, keeping recruitment and retention firmly center stage. Jay Gurney, director of radiology for Projectus

Regulation and Reimbursement: As hospital-based interventional procedures face their steepest revenue declines, groups with a strong IR background are recognizing the value of expanding into office-based labs as a necessary step to protect and diversify their revenue streams. Sandy Coffta, VP of client services at Healthcare Administrative Partners

Theranostics Emerges as Primary Treatment Option: Theranostics will continue to shed its reputation as a last-ditch treatment option, and dosimetry will become more routine. This upstream migration will be accelerated by emerging medium axial-field-of-view PET/CT scanners (~24-48 cm), which will increasingly deliver the sensitivity needed to reveal micrometastases missed by standard PET/CT systems, justifying earlier deployment of molecular therapies. Eliot Siegel, MD, professor, University of Maryland and co-founder of United Theranostics

The Takeaway

As healthcare’s most dynamic medical specialty, radiology is sure to continue its rapid pace of evolution in 2026. Rest assured that you can read about all the year’s top radiology trends in The Imaging Wire.

Canon Celebrates 50 Years of CT Innovation: Redefining Healthcare with Meaningful AI

Posted on January 7, 2026January 7, 2026 by Partner Content

This year marks a historic milestone for Canon – five decades of pioneering CT innovation that has transformed the landscape of healthcare. From introducing industry-first technologies to setting new standards in diagnostic imaging, Canon continues to lead the way in delivering solutions that matter.

Canon’s legacy is built on breakthroughs such as its three-time award-winning wide-area CT systems, deep learning reconstruction that brings 1K resolution to CT imaging, and automation improving workflow.

These innovations have consistently elevated diagnostic confidence, patient safety, and operational efficiency.

In today’s world, AI is everywhere – but Canon’s AI is Meaningful AI. It’s not about AI for the sake of technology; it’s about creating real-world impact on patient care.

Canon’s portfolio of scanner-integrated AI applications is designed to enhance image quality, streamline workflows, and improve consistency – ultimately delivering better care, better experience, and better efficiency for patients and providers alike.

Canon is redefining CT by making AI a core component across its portfolio. Key innovations include…

AI-Assisted Scanner Workflow Automation. Canon’s INSTINX platform introduces intuitive, intelligent, and integrated AI technologies that enable autonomous CT operations. By simplifying complex workflows, INSTINX helps technologists focus on patient care while improving throughput and reducing variability.
AI-Assisted Post-Processing. Canon’s Automation Platform offers a zero-click, AI-driven solution that accelerates image post-processing. By delivering fast, actionable insights, this platform ensures time-critical results reach care teams when they need them most.

AI-Assisted Reconstruction. Advanced algorithms such as AiCE DLR and PIQE DLR leverage deep learning to reveal critical diagnostic information – contrast and resolution – while optimizing dose efficiency. These tools empower clinicians to make confident diagnoses and reduce the need for additional downstream studies. Additionally, CLEARMotion, a DCNN-based algorithm, compensates for patient motion, reducing blur and delivering high-quality results even in challenging cases.

The Takeaway

As Canon celebrates 50 years of CT innovation, its commitment remains clear: harnessing AI to make imaging smarter, faster, and more meaningful. With these advancements, Canon is not just shaping the future of CT – it’s setting a new benchmark for patient-centered care.

VC Radiology Funding Drops

Posted on January 4, 2026January 2, 2026 by Brian Casey

Venture capital investment in radiology peaked in 2021 at just over $2B and has been on a slow decline since then. That’s according to a study in JACR that documents the ebb and flow of VC investment, in particular its shift to companies developing AI algorithms.

VC investment is the lifeblood of any industry built on innovation, and healthcare is no exception.

Venture capital funding helps many innovators bring their ideas to fruition and helps fund them until revenue from product sales can start rolling in.

So it stands to reason that changes in VC funding levels can have ripple effects, with declines potentially affecting the rate of new technology development.

Indeed, some studies have found that every 1% increase in interest rates can cause a 3% decline in R&D spending and a 9% drop in patent filings.

The new research tracks VC funding specifically in radiology, with researchers from Emory and Harvard universities using PitchBook to track VC investments from 2000 to 2023.

In particular, researchers found…

A total of $11.4B was invested in 646 radiology companies during the entire study period.
The average investment was $6.3M with an average $51M post-investment valuation.
VC investment activity in radiology peaked in 2021 at $2.18B.
Medical devices attracted 28% of investment, followed by AI healthcare software (22%), non-AI healthcare software (18%), healthcare services (14%), and biotechnology and drug discovery (18%).

The new data track with research from other sources – like Signify Research – that have also documented a slowdown in radiology VC investment, particularly in AI.

Most sources attribute the declines to the end of the “cheap money” era during the COVID-19 pandemic as governments began dialing back on stimulus payments and started raising interest rates to tamp down inflation. On the other hand, other research has found that the recent declines are occurring at a rate that’s not proportional to inflation or interest rates alone.

The Takeaway

The new JACR research comes as the investment and healthcare worlds are set to begin their annual courtship ritual next week at the J.P. Morgan Healthcare Conference in San Francisco. Undoubtedly these new findings will be a point of discussion as radiology companies look to secure the capital that will fuel the next innovations in medical imaging.

Risk-Based Mammo Screening – Ready for Prime Time?

Posted on December 14, 2025December 13, 2025 by Brian Casey

Is mammography screening based on patient risk ready to take over for age-based screening? Results from the WISDOM study presented at last week’s San Antonio Breast Cancer Symposium and published simultaneously in JAMA suggest that while risk-based screening has its merits, more work may need to be done.

Cancer screening exams like mammography have reduced disease-specific mortality, but (with the exception of lung cancer screening) all use exclusively age-based criteria to determine who should get screened.

Age isn’t a great tool for determining who’s at higher risk of getting cancer, but it’s the best tool we’ve had – up to now.

New cancer risk prediction tools are now becoming available, prompting debate over whether these techniques could make screening more precise by directing it to those most at risk.

Higher-risk people could get more frequent screening, while lower-risk individuals might be directed to longer screening intervals.

The WISDOM study presented at SABCS 2025 investigates this question. WISDOM is a randomized clinical trial that compared risk-based breast screening to age-based annual screening in 28.4k women followed for five years.

Risk categorization was performed with genetic testing, polygenic risk scores, and BCSC scores, which incorporate family history and imaging results.

Women in the risk-based screening group were directed into one of four screening strategies, from alternating mammography and MRI every six months for high-risk women to no screening until age 50 for low-risk women.

The study’s primary outcomes were detection rates for breast cancers rated as stage IIB or higher and effectiveness in reducing biopsy rates – a proxy for screening-caused morbidity.

Across the study population, researchers found…

The rate of mammograms per 100k person-years was lower in the risk-based cohort compared to age-based screening (43.1k vs. 46.9k).
The rate of stage IIB or higher cancers per 100k person-years was also lower in the risk-based cohort (30 vs. 48).
But there was no statistically significant difference in biopsy rates, with a rate difference of 99 per 100k person-years (p = 0.10).

One problem with the WISDOM trial was that the actual screening exams were performed outside the study, and some patients did not comply with screening recommendations, potentially confounding results.

The Takeaway

The WISDOM authors concluded that a risk-based screening approach is safe, but the lack of a difference in biopsy rates makes one wonder if veering from established age-based criteria is worth it. In any event, the coming arrival of risk stratification based on AI mammogram analysis could make the genetic testing-based approach used in WISDOM obsolete.

FDA AI Approvals Surge Past 1k for Radiology

Posted on December 10, 2025December 19, 2025 by Brian Casey

The number of AI-enabled medical devices granted FDA marketing authorization for radiology surged past the 1k mark in the latest update from the agency. The numbers show that radiology’s share of authorizations remains stable at just over three-quarters of total approvals.

The FDA regularly releases the list in what’s become a closely watched barometer of both total approvals as well as which medical specialties are most active in AI.

Radiology has historically garnered the lion’s share of approvals – perhaps no surprise given the discipline’s early adoption of both digital image management and AI – with the first authorization granted in 1998 (for ImageChecker mammography CAD from R2 Technology/Hologic).

The new list tracks authorizations through the end of September 2025, and indicates the agency has…

Authorized 1,356 AI-enabled devices since it started tracking, up 8.5% since its last report.
Approved 1,039 AI-enabled radiology devices, with imaging accounting for 77% of total medical authorizations since 1998.
Radiology secured 75% of total authorizations from June to September (83/110), compared to 78% from January to May 2025, 73% for all of 2024, and 80% for 2023.
GE HealthCare retains the top spot as the company with the most radiology AI authorizations, at 115 (including recent acquisitions Bay Labs, BK Medical, Caption Health, MIM Software, icometrix, and Spectronic Medical).
Next is Siemens Healthineers at 86 (including Varian), then Philips at 48 (including DiA Analysis and TomTec), Canon at 41 (including Vital Images and Olea), United Imaging at 38, and Aidoc at 30.

As always, it’s worth noting that the FDA’s list includes not only standalone software applications, but also imaging equipment that might have AI applications embedded into it, such as a mobile X-ray system with AI algorithms for detecting emergent conditions.

Also, the agency noted that it is exploring ways to identify and tag AI-based devices that use foundation models and large language models. The FDA has yet to approve an LLM-based medical device.

The Takeaway

The new numbers indicate that radiology’s dominance of medical AI continues. But they also show that the FDA has returned to a regular twice-yearly cadence of updating its list of AI-enabled medical devices after a break of nearly a year – news that’s welcome to AI developers.

Risks of Rising Contrast Use

Posted on December 7, 2025December 7, 2025 by Brian Casey

The use of contrast media in medical imaging procedures has been rising steadily in recent years, a trend that creates environmental risks. So says a new study in JAMA Network Open that documents growth in contrast use over the past 13 years.

Contrast is an essential part of many imaging exams, helping radiologists better visualize pathology that might be harder to see on unenhanced scans.

But contrast use also comes with a wide array of risks, from patient reactions that on rare occasions can be fatal to environmental buildup of contrast that’s excreted from patients after exams and makes its way into local waterways – including drinking water.

This latter phenomenon is what’s explored in the new paper, authored by researchers from the ACR’s Harvey L. Neiman Health Policy Institute.

They analyzed Medicare claims from 2011 to 2024 for 169M contrast-enhanced imaging exams that involved the use of 13.5B milliliters of contrast for both CT and MRI studies.

HPI’s analysis found…

Iodinated CT contrast use grew 5.2% and gadolinium MRI was up 3.5% from 2014 to 2019.
Contrast use fell 9.6% for CT and 15.6% for MRI during the COVID-19 pandemic in 2020, but then rebounded afterward.
A small number of exams accounted for most of the CT contrast usage, such as CT abdomen and pelvis (4.4B mL) and CT chest (2.7B mL).
MRI numbers were far lower, such as for brain MRI scans (221M mL) and abdominal MRI studies (70M).

So what can radiology do? Simply reducing contrast use for environmental reasons isn’t much of a solution, as it has implications not only for patient care but also for medical malpractice risk.

But ongoing efforts to reduce inappropriate imaging would have a follow-on effect of also lowering contrast use, as would protocols to reduce contrast use for patient safety reasons (the introduction of high-relaxivity gadolinium-based agents that cut MRI contrast dose by 50% is a great example).

The authors also cite the development of AI-based techniques that could create contrast-like exams from existing non-contrast data, offering AI developers another possible segment to target.

The Takeaway

The new study offers an interesting twist in the debate over contrast reduction, pointing out that efforts to reduce unnecessary contrast use promise to benefit not only patients but also the planet.

Snow Doesn’t Slow RSNA 2025

Posted on December 3, 2025December 3, 2025 by Brian Casey

RSNA 2025 is wrapping up this week in what’s been a cold and snowy Chicago. While many attendees experienced travel delays getting into the show on Sunday, the disruptions didn’t slow the blistering pace of radiology innovation on display.

As has been the case all year in radiology, AI has been a hot topic at McCormick Place, both in the presentation rooms and on the technical exhibit floor.

Much of the conversation is shifting away from individual point sources of AI – such as for analyzing images – and toward solutions that provide operational efficiencies such as faster radiology reporting.

But big iron has always been RSNA’s bread and butter, and RSNA 2025 didn’t disappoint.

Major new product launches took place in the vendor exhibits, especially in helium-free MRI, photon-counting and spectral CT, and angiography, showing that vendors continue to invest in hardware development. Check out our coverage of the major OEMs in The Wire section below.

What were the other trends at RSNA 2025? They included…

Growing buzz around new AI technologies like foundation and vision language models.
Real-world clinical applications of AI such as triaging mammography screening.
Growing momentum of CT lung cancer screening, both internationally and in the U.S.
Use of generative AI to improve radiology reporting.
Imaging’s contribution to greenhouse gas emissions – and how to reduce them.
Imaging-based biomarkers that can predict future disease incidence.
Opportunistic screening with imaging tests that can detect multiple diseases in one exam.

The Takeaway

Despite weather-related challenges, RSNA 2025 once again showed the importance of radiology’s showcase annual conference for bringing together academics, private-practice providers, vendors, and allied health professionals to meet, exchange ideas, and work together toward providing better patient care. It was great seeing everyone in Chicago – safe travels home!

CT Lung Screening Leads RSNA’s First Day

Posted on November 30, 2025November 30, 2025 by Brian Casey

Day 1 of RSNA 2025 is in the books, and new research into CT lung cancer screening dominated the scientific sessions at Chicago’s McCormick Place.

Lung cancer screening is drawing attention as screening programs go into effect internationally.

In the U.S., lung screening is hampered by low completion rates (18-19%), but providers are finding that participation can be improved with aggressive identification and outreach to eligible patients.

Some highlights from Sunday (with handy session numbers to help you follow along) include…

The ScreenLungNet AI model predicted three-year lung cancer risk from CT lung screening scans with AUCs from 0.93-0.94 (S4-SSCH02-1).
In a study of 2.6k patients with lung cancer, only 36% met 2021 USPSTF lung cancer screening criteria, and just 5% actually got screened. Only 23% had data on their smoking history in the EMR (S4-SSCH02-2).
Risk assessment scores were used to perform CT lung screening of lower-risk people every two years rather than annually, reducing screening’s harms without missing many cancers (S4-SSCH02-4).
Compared to the landmark NLST study, a real-world CT lung screening program had fewer benign surgeries (12% vs. 18%), lower complication rates (24% vs. 32%), and better recurrence-free survival (HR = 0.60) (S4-SSCH02-5).
CT radiation dose was reduced 51% and contrast iodine use 61% through a triple-optimized protocol that included 80-kVp scanning, GE HealthCare’s TrueFidelity deep learning reconstruction, and low-iodine adaptive contrast injection (S2-SSCA01-1).
Using AI for automated patient positioning and scan range in CT exams cut positioning time 41% with 10-13% lower radiation dose and no discernible impact on image quality (S4-SSIN01-1).
Measures of adiposity acquired opportunistically from coronary artery calcium CT scans using HeartLung Technologies’ AI-CVD algorithm predicted adults at risk of diabetes in a study of 2.9k people (S5-SSCA02-6).
The Promedius AI algorithm for osteoporosis assessment of chest radiographs had an AUC of 0.84 in a study of 1k adults from three countries (M3-SSCH03-1).
A real-world study of 2.1k patients found that DeepTek’s chest X-ray AI algorithm had an AUROC of 0.95 for detecting any of 13 clinically significant findings (M3-SSCH03-2).
Researchers presented a feasibility study of a compression-free spectral DBT mammography system, finding spatial resolution close to state-of-the-art systems (S4-SSPH02-6).
Researchers presented their protocol for MRI scanning of patients with cardiac implanted electronic devices. Over 10 years they scanned 7.3k patients with no major adverse events (S5-SSCA02-1).
Adding MRI data to a multimodal transformer AI model improved its ability to predict five-year breast cancer risk in intermediate- and high-risk women (S2-SSBR01-6).

The Takeaway

RSNA 2025 is off to a great start. Be sure to check back with Thursday’s newsletter for more radiology news from Chicago, and follow along on our social media channels for ongoing video updates.

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