Tag: imaging AI

Top 12 Radiology Trends for 2024

Posted on by Brian Casey

What will be the top radiology trends for 2024? We talked to key opinion leaders across the medical imaging spectrum to get their opinions on the technologies, clinical applications, and regulatory developments that will shape the specialty for the next 12 months.

AI – Generative AI to Reduce Radiology’s Workload: “New generative AI methods will summarize complex medical records, draft radiology reports from images, and explain radiology reports to patients using language they understand. These innovative systems will reduce our workload and will provide more time for us to connect with our colleagues and our patients.” — Curtis Langlotz, MD, PhD, Stanford University and president, RSNA 2024

AI – Generative AI Will Get Multimodal: “In 2024, we can expect continued innovations in generative AI with a greater emphasis on integrating GenAI into existing and new radiology and patient-facing applications with growing interests in retrieval-augmented generation, fine-tuning, smaller models, multi-model routing, and AI assistants. Medicine being multimodal, the term ‘multimodal’ will become more ubiquitous.” — Woojin Kim, MD, CMIO at Rad AI

AI – Will AI Really Reduce Radiology Burnout? “Burnout will continue to be a huge issue in radiology with no solution in sight. AI vendors will offer algorithms as solutions to burnout with catchy slogans such as ‘buy our lung nodule detector and become the radiologist your parents wanted you to be.’ Their enthusiasm will cause even more burnout.” — Saurabh Jha, MBBS, AKA RogueRad, Hospital of the University of Pennsylvania

Breast Imaging – Prepare Now for Density Reporting: “The FDA ‘dense breast’ reporting standard to patients becomes effective on September 10, 2024, and breast imaging centers should be prepared for new patient questions and conversations. A plan for a consistent approach to recommending supplemental screening and facilitating ordering of additional imaging from referring providers should be put into action.” — JoAnn Pushkin, executive director, DenseBreast-info.org

Breast Imaging – Density Reporting to Spur Earlier Detection: “In March 2023, FDA issued a national requirement for reporting breast density to patients and referring providers after mammography. Facilities performing mammograms must meet the September 2024 deadline incorporating breast density type and associated breast cancer risk in their reporting. This change can lead to earlier breast cancer detection as these patients will be informed of supplemental screening as it relates to their breast density and [will] choose to pursue it.” — Stamatia Destounis, MD, Elizabeth Wende Breast Care and chair, ACR Breast Imaging Commission

CT – Lung Cancer Screening to Build Momentum: “Uptake of LDCT screening for lung cancer will increase in the US and worldwide. AI-enabled cardiac evaluation, even on non-gated scans, will allow for prediction of illnesses such as AFib and heart failure.  Quantifying measurement error across platforms will become an important aspect of nodule management.” — David Yankelevitz, MD, Icahn School of Medicine at Mount Sinai Health System

CT – Photon-Counting CT to Expand: “In 2024, we will continue to see many papers published on photon-counting CT, strengthening the body of scientific evidence as to its many strengths. Results from clinical trials involving multiple manufacturers’ systems will also increase in number, perhaps leading to more commercial systems entering the market.” — Cynthia McCollough, PhD, director, CT Clinical Innovation Center, Mayo Clinic

Enterprise Imaging – Time is Ripe for Cloud and AI: “Healthcare has an opportunity for change in 2024, and imaging is ripe for disruption, with burnout, staffing challenges, and new technology needs. Many organizations are expanding their enterprise imaging strategy and are asking how and where they can take the plunge into cloud and AI. Vendors have got the message; now it’s time to push the gas and deliver.” — Monique Rasband, VP of strategy & research, imaging/oncology at KLAS

Imaging IT – Data Brokerage to Go Mainstream: “A new market will hit the mainstream in 2024 – radiology data brokerage. As data-hungry LLMs scale up and the use of companion diagnostics in lifesciences proliferates, health systems will look to cash in on curated radiology data. This will also be an even bigger driver for migration to cloud-based imaging IT.” — Steve Holloway, managing director, Signify Research     

MRI – Prostate MRI to Reduce Biopsies: “Prostate MRI in conjunction with PSMA PET will explode in 2024 and reduce the number of unnecessary biopsies for patients.” — Stephen Pomeranz, MD, CEO of ProScan Imaging and chair, Naples Florida Community Hospital Network 

Theranostics – New Radiotracers to Drive Diagnosis & Treatment: “Through 2024, nuclear medicine theranostics will increasingly be integrated into standard global practice. With many new radiopharmaceuticals in development, theranostics promise early diagnosis and precision treatment for a broadening range of cancers, expanding options for patients resistant to traditional therapies. Treatments will be enhanced by personalized dosimetry, artificial intelligence, and combination therapies.” — Helen Nadel, MD, Stanford University and president, SNMMI 2023-2024

Radiology Operations – Reimbursement Challenges Continue: “In 2024, we will continue to experience recruitment challenges coupled with decreases in reimbursement. Now, more than ever, every radiologist needs to be diligent in advocating for the specialty, focus on business plan diversification, and ensure all services rendered are optimally documented and billed.” — Rebecca Farrington, chief revenue officer, Healthcare Administrative Partners 

The Takeaway
To paraphrase Robert F. Kennedy, radiology is indeed living in interesting times – times of “danger and uncertainty,” but also times of unprecedented creativity and innovation. In 2024, radiology will get a much better glimpse of where these trends are taking us.

Lunit’s Deal for Volpara and AI Consolidation

Posted on by Brian Casey

Is the long-awaited consolidation of the healthcare AI sector gaining steam? In a deal valued at close to $200M, South Korean AI developer Lunit announced a bid to acquire Volpara Health, a developer of software for calculating breast density and cancer risk. 

At first glance, the alliance seems to be a match made in heaven. Lunit is a well-regarded AI developer that has seen impressive results in clinical trials of its Insight family of algorithms for indications ranging from mammography to chest imaging. 

  • Most recently, Lunit received FDA clearance for its Insight DBT software, marking its entry into the US breast screening market, and it also raised $150M in a public stock offering. 

Volpara has a long pedigree as a developer of breast imaging software, although it has shied away from image analysis applications to instead focus on breast center operations and risk assessment, in particular by calculating breast density. 

  • Thus, combining Lunit’s concentration in image analysis with Volpara’s focus on operations and risk assessment enables the combined company to offer a wider breadth of products to breast centers.

Lunit will also be able to take advantage of the marketing and sales structure that Volpara has built in the US mammography sector (97% of Volpara’s sales come from the US, where it has an installed base of 2k sites). Volpara expects 2024 sales of $30M and is cash-flow positive.

The question is whether the acquisition is a sign of things to come in the AI market. 

  • As commercial AI sales have been slow to develop, AI firms have largely funded their operations through venture capital firms – which are notoriously impatient in their quest for returns.

In fact, observers at the recent RSNA 2023 meeting noted that there were very few new start-up entrants into the AI space, and many AI vendors had smaller booths. 

  • And previous research has documented a slowdown in VC funding for AI developers that is prompting start-up firms to seek partners to provide more comprehensive offerings while also focusing on developing a road to profitability. 

The Takeaway

It’s not clear yet whether the Lunit/Volpara deal is a one-off combination or the start of a renewed consolidation trend in healthcare AI. Regardless of what happens, this alliance unites two of the stronger players in the field and has exciting potential for the years to come. 

AI’s Impact on Breast Screening

Posted on by Brian Casey

One of the most exciting radiology use cases for AI is in breast screening. At last week’s RSNA 2023 show, a paper highlighted the technology’s potential for helping breast imagers focus on cases more likely to have cancer.

Looking for cancers on screening mammography has been compared to finding a needle in a haystack, and as such it’s considered to be one of the areas where AI can best help. 

  • One of the earliest use cases was in identifying suspicious breast lesions during radiologist interpretation (remember computer-aided detection?), but more recently researchers have focused on using AI as a triage tool, by identifying cases most likely to be normal that could be removed from the radiologist’s urgent worklist. Studies have found that 30-40% of breast screening cases could be read by AI alone or triaged to a low-suspicion list.

But what impact would AI-based breast screening triage have on radiologist metrics such as recall rate? 

  • To answer this question, researchers from NYU Langone Health prospectively tested their homegrown AI algorithm for analyzing DBT screening cases.

The algorithm was trained to identify extremely low-risk cases that could be triaged from the worklist while more complex cases where the AI was uncertain were sent to radiologists, who knew in advance the cases they were reading were more complicated. In 11.7k screening mammograms, researchers examined recall rates over two periods, one before AI triage and one after, finding: 

  • The overall recall rate went from 13% before the triage period to 15% after 
  • Recall rates for complex cases went from 17% to 20%
  • Recall rates for extremely low-risk studies went from 6% to 5%
  • There were no statistically significant differences in any of the comparisons
  • No change in median self-reported perceived difficulty of reading from the triage lists compared to non-triage list, regardless of years of experience

In future work, the NYU Langone researchers will continue their study to look at AI’s impact on cancer detection rate, biopsy rate, positive predictive value, and other metrics.

The Takeaway

The NYU Langone study puts a US spin on research like MASAI from Sweden, in which AI was able to reduce radiologists’ breast screening workload by 44%. Given the differences in screening protocols between the US and Europe, it’s important to assess how AI affects workload between the regions.

Further work is needed in this ongoing study, but early results indicate that AI can triage complex cases without having an undue impact on recall rate or self-perceived difficulty in interpreting exams – a surrogate measure for burnout.

RSNA 2023 Video Highlights

Posted on by Brian Casey

That’s a wrap! 

RSNA 2023 just concluded, and by most accounts it was a successful conference. Preliminary figures indicate that attendance was up 11% over 2022. While short of the glory days of RSNA, the numbers indicate that the meeting’s recovery from the COVID-19 pandemic will be slow but steady.

As expected, AI was a dominant theme at McCormick Place, and that’s reflected in our video coverage of the technical exhibit floor. AI busted out of the AI Showcase to permeate both exhibit halls, a sign of the technology’s growing influence on radiology.

We profiled many of the most intriguing companies that were exhibiting at RSNA 2023 – some of them dominant players in the field while others are new entries looking to secure a foothold. 

We hope you enjoy watching our coverage as much as we enjoyed producing it! Check out the links below or visit the Shows page on our website.

AI’s Incremental Revolution

Posted on by Brian Casey

So AI dominated the discussion at last week’s RSNA 2023 meeting. But does that mean it’s finally on the path to widespread clinical use? 

Maybe not so much. For a technology that’s supposed to have a revolutionary impact on medicine, AI is taking a frustratingly long time to arrive. 

Indeed, there was plenty of skepticism about AI in the halls of McCormick Place last week. (For two interesting looks at AI at RSNA 2023, also see Hugh Harvey, MD’s list of takeaways in a post on X/Twitter and Herman Oosterwijk’s post on LinkedIn.) 

But as one executive we talked to pointed out, AI’s advance to routine clinical use in radiology is likely to be more incremental than all at once. 

  • And from that perspective, last week’s RSNA meeting was undoubtedly positive for AI. Scientific sessions were full of talks on practical clinical applications of AI, from breast AI to CT lung screening. 

Researchers also discussed the use of AI apart from image interpretation, with generative AI and large language models taking on tasks from answering patient questions about their reports to helping radiologists with dictation.

It’s fine to be a skeptic (especially when it comes to things you hear at RSNA), but for perspective look at many of the past arguments casting doubt on AI: 

  • AI algorithms don’t have FDA clearance (the FDA authorized 171 algorithms in just the past year)
  • You can’t get paid for using AI clinically (16 algorithms have CPT codes, with more on the way) 
  • There isn’t enough clinical evidence backing the use of AI (tell that to the authors of MASAI, PERFORMS, and a number of other recent studies with positive findings)
  • The AI market is overcrowded with companies and ripe for consolidation (what exciting new growth market isn’t?)

The Takeaway

Sure, it’s taking longer than expected for AI to take hold in radiology. But last week’s conference showed that AI’s incremental revolution is not only advancing but expanding in ways no one expected when IBM Watson was unveiled to an RSNA audience a mere 6-7 years ago. One can only imagine what the field will look like at RSNA 2030.

Looking for more coverage of RSNA 2023? Be sure to check out our videos from the technical exhibit floor, which you can find on our new Shows page.

AI Dominates at RSNA 2023

Posted on by Brian Casey

Take a deep breath. You survived another RSNA conference.

While a few hardy souls are still enjoying educational sessions in the cozy confines of McCormick Place, the final day of the exhibit floor yesterday marks the end of RSNA 2023 for most attendees. And what a show it was. 

Predictions were that AI would dominate the scientific sessions at RSNA 2023, a forecast that largely panned out. A November 28 session was a case in point, in which a series of top-quality papers were presented on one of the most promising use cases of AI, for breast screening:

  • A homegrown AI algorithm that analyzed screening breast ultrasound exams in addition to FFDM and DBT mammograms boosted sensitivity for detecting cancer in 12.5k patients, with better sensitivity for women with dense breasts (71% vs. 60%) and non-dense breasts (79% vs. 63%)
  • AI did a good job of detecting breast arterial calcification (BAC) when used prospectively to analyze screening mammograms in 16k women across 15 sites.  It found 15% of women had BAC, a possible marker for atherosclerotic disease
  • Swedish researchers used their VAI-B validation platform to compare three AI algorithms (Therapixel, Lunit, and Vara) in 34k women, finding that using AI with a single radiologist boosted sensitivity 10-30% compared to double reading, with a slight loss in specificity (2-7%). VAI-B could be used to validate AI implementation and guide purchasing decisions
  • Why does AI miss some breast cancers? South Korean researchers addressed this question by analyzing 1.1k patients with invasive cancers in which AI had a miss rate of 14%. Luminal cancers were missed most often
  • Adding AI analysis of prior images to current studies with FFDM and DBT boosted sensitivity for cancer detection in 30k patients, with sensitivity the highest for two years of priors compared to no priors (74% vs. 70%)

The Takeaway

This week’s research points to an exciting near-term future in which AI will help make mammography screening more accurate while helping breast radiologists perform their jobs more efficiently. Landmark studies toward this end were published in 2023 – this week’s RSNA conference shows that we can expect the momentum to continue in 2024. 

Reimbursement Drives AI Adoption

Posted on by Brian Casey

It’s no secret that insurance reimbursement drives adoption of new medical technology. But a new analysis in NEJM AI shows exactly how reimbursement is affecting the diffusion into clinical practice of perhaps the newest medical technology – artificial intelligence. 

Researchers analyzed a database of over 11B CPT claims from January 2018 to June 2023 to find out how often reimbursement claims are being submitted for the use of the over 500 AI devices that had been approved by the FDA at the time the paper was finalized. 

  • The authors chose to focus on CPT claims rather than claims under the NTAP program for new technologies because CPT codes are used by both public and private payors in inpatient and outpatient settings, while NTAP only applies to Medicare inpatient payments. 

They found 16 medical AI procedures billable under CPT codes; of these, 15 codes were created since 2021 and the median age of a CPT code was about 374 days, indicating the novelty of medical AI.

  • Also, only four of the 16 had more than 1k claims submitted, leading the authors to state “overall utilization of medical AI products is still limited and focused on a few leading procedures,” such as coronary artery disease and diabetic retinopathy.

The top 10 AI products and number of CPT claims submitted are as follows:

  1. HeartFlow Analysis for coronary artery disease (67,306)
  2. LumineticsCore for diabetic retinopathy (15,097)
  3. Cleerly for coronary atherosclerosis (4,459)
  4. Perspectum LiverMultiScan for liver MRI (2,428)
  5. Perspectum CoverScan for multiorgan MRI (591)
  6. Koios DS for breast ultrasound (552)
  7. Anumana for ECG cardiac dysfunction (435)
  8. CADScor for cardiac acoustic waveform recording (331)
  9. Perspectum MRCP for quantitative MR cholangiopancreatography (237)
  10. CompuFlo for epidural infusion (67)

While radiology may rule in terms of the sheer number of FDA-approved AI products (79% in a recent analysis), the list shows that cardiology is king when it comes to paying the bills. 

The Takeaway

Amid the breathless hype around medical AI, the NEJM AI study comes as a bit of a wake-up call, showing how the cold reality of healthcare economics can limit technology diffusion – a finding also indicated in other studies of economic barriers to AI. 

On the positive side, it shows that a rosy future lies ahead for those AI algorithms – like HeartFlow Analysis – that can make the leap.

Accessing Quality Data for AI Training

Posted on by Partner Content

One of the biggest roadblocks in medical AI development is the lack of high-quality, diverse data for these technologies to train on.

What Is the Issue with Data Access?

Artificial Intelligence (AI) has emerged as a game-changer in the realm of medical imaging, with immense potential to revolutionize clinical practices. AI-powered medical imaging can efficiently identify intricate patterns within data and provide quantitative assessments of disease biomarkers. This technology not only enhances the accuracy of diagnosis but can also significantly speed up the diagnostic process, ultimately improving patient outcomes.

While the landscape is promising, medical innovators grapple with challenges in accessing high-quality, diverse, and timely data, which is vital for training AI and driving progress.

A 2019 study from the Massachusetts Institute of Technology found that over half of medical AI studies predominantly relied on databases from high-income countries, particularly the United States and China. If models trained on homogenous data are used clinically in diverse populations, then it could pose a risk to patients and worsen health inequalities experienced by underrepresented groups. In the United States, If the Food and Drug Administration deems these risks to be too high, then they could even reject a product’s application for approval. 

In trying to get hold of the best training data, AI developers, particularly startups and individual researchers, face a web of complexities, including legal, ethical, and technical considerations. Issues like data privacy, security, interoperability, and data quality compound these challenges, all of which are crucial in the effective and responsible utilization of healthcare data.

One company working to overcome these hurdles in hope of accelerated and high-quality innovations is Gradient Health.

Gradient Health’s Approach

Gradient Health offers AI developers instant access to one of the world’s largest libraries of anonymized medical images, sourced from hundreds of global hospitals, clinics, and research centers. This data is meticulously de-identified for compliance and can be tailored by vendors to suit their project’s needs and exported in machine learning-ready DICOM + JSON formats.

By partnering with Gradient Health, innovators can use these extensive, diverse datasets to train and validate their AI algorithms, mitigating bias in medical AI and advancing the development of precise, high-quality medical solutions.

Gaining access to top-tier data at the outset of the development process promises long-term benefits. Here’s how:

  • Expand Market Presence: Access the latest cross-vendor datasets to develop medical innovations, expanding your market share.
  • Global Expansion: Enter new regions swiftly with locally sourced data from your target markets, accelerating your global reach.
  • Competitive Edge: Obtain on-demand training data for imaging modalities and disease areas, facilitating product portfolio expansion.
  • Speed to Market: Quickly acquire data for product training and validation, reducing sourcing time and expediting regulatory clearances for faster patient delivery.

“After looking for a data provider for many weeks, I was not able to get even a sample delivery within one month. I was immensely glad to work with Gradient and go from first contact to final delivery within one week!” said Julien Schmidt, chief operations officer and co-founder at Mango Medical.

The Outlook

In recent years, medical AI has experienced significant growth. Innovations in medical imaging in particular have played a pivotal role in enabling healthcare professionals to identify diseases earlier and more accurately in patients with a range of conditions. 

Gradient Health offers a data-compliant, intuitive platform for AI developers, facilitating access to the essential data required to train these critical technologies. This approach holds the potential to save time, resources, and, most importantly, lives. 

More information about Gradient Health is available on the company’s website. They will also be exhibiting at RSNA 2023 in booth #5149 in the South Hall.

Unpacking the Biden Administration’s New AI Order

Posted on by Brian Casey

It seems like watershed moments in AI are happening on a weekly basis now. This time, the big news is the Biden Administration’s sweeping executive order that directs federal regulation of AI across multiple industries – including healthcare. 

The order comes as AI is becoming a clinical reality for many applications. 

  • The number of AI algorithms cleared by the FDA has been surging, and clinicians – particularly radiologists – are getting access to new tools on an almost daily basis.

But AI’s rapid growth – and in particular the rise of generative AI technologies like ChatGPT – have raised questions about its future impact on patient care and whether the FDA’s existing regulatory structure is suitable for such a new technology. 

The executive order appears to be an effort to get ahead of these trends. When it comes to healthcare, its major elements are summarized in a succinct analysis of the plan by Health Law Advisor. In short, the order: 

  • Calls on HHS to work with the VA and Department of Defense to create an HHS task force on AI within 90 days
  • Requires the task force to develop a strategic plan within a year that could include regulatory action regarding the deployment and use of AI for applications such as healthcare delivery, research, and drug and device safety
  • Orders HHS to develop a strategy within 180 days to determine if AI-enabled technologies in healthcare “maintain appropriate levels of quality” – basically, a review of the FDA’s authorization process
  • Requires HHS to set up an AI safety program within a year, in conjunction with patient safety organizations
  • Tells HHS to develop a strategy for regulating AI in drug development

Most analysts are viewing the executive order as the Biden Administration’s attempt to manage both risk and opportunity. 

  • The risk is that AI developers lose control of the technology, with consequences such as patients potentially harmed by inaccurate AI. The opportunity is for the US to become a leader in AI development by developing a long-term AI strategy. 

The Takeaway

The question is whether an industry that’s as fast-moving as AI – with headlines changing by the week – will lend itself to the sort of centralized long-term planning envisioned in the Biden Administration’s executive order. Time will tell.

Predicting the Future of Radiology AI

Posted on by Brian Casey

Making predictions is a messy business (just ask Geoffrey Hinton). So we’re always appreciative whenever key opinion leaders stick their necks out to offer thoughts on where radiology is headed and the major trends that will shape the specialty’s future. 

Two of radiology’s top thought leaders on AI and imaging informatics – Curtis Langlotz, MD, PhD, and Paul Chang, MD – gaze into the crystal ball in two articles published this week in Radiology as part of the journal’s centennial celebration. 

Langlotz offers 10 predictions on radiology AI’s future, briefly summarized below:

  • Radiology will continue its leadership position when it comes to AI adoption in medicine, as evidenced by its dominance of FDA marketing authorizations
  • Virtual assistants will help radiologists draft reports – and reduce burnout
  • Radiology workstations will become cloud-based cockpits that seamlessly unify image display, reporting, and AI
  • Large language models like ChatGPT will help patients better understand their radiology reports
  • The FDA will reform its regulation of AI to be more flexible and speed AI authorizations (see our article in The Wire below)
  • Large databases like the Medical Imaging and Data Resource Center (MIDRC) will spur data sharing and, in turn, more rapid AI development

Langlotz’s predictions are echoed by Chang’s accompanying article in Radiology in which he predicts the future of imaging informatics in the coming age. Like Langlotz, Chang sees the new array of AI-enabled tools as beneficial agents that will help radiologists manage growing workloads through dashboards, enhanced radiology reports, and workflow automation. 

The Takeaway

This week’s articles are required reading for anyone following the meteoric growth of AI in radiology. Far from Hinton’s dystopian view of a world without radiologists, Langlotz and Chang predict a future in which AI and IT technologies assist radiologists to do their jobs better and with less stress. We know which vision we prefer.

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