Breast screening AI can improve cancer detection and workflow efficiency, but choosing the right solution requires more than comparing features. Discover 10 practical strategies to evaluate AI investments, avoid common pitfalls, and build a connected breast imaging ecosystem designed for long-term success.
Becky Weber, Executive Vice President, Sales, Lunit International
What this covers
This guide outlines how healthcare leaders can evaluate and invest in breast screening AI to improve workflow efficiency, clinical consistency, and long-term flexibility.
Key takeaway
The most effective breast screening AI strategies focus on connected workflows and full pathway coverage, not standalone tools.
Breast imaging centers are often built on a patchwork of tools layered over time, with each one solving a specific need but rarely designed to work together. The rapid growth of AI in healthcare – with more than 1,500 AI-enabled devices authorized by the FDA1 – has expanded what’s possible for clinicians, but it has also increased the number of applications that teams must navigate. The result can be a fragmented experience that makes workflows harder to streamline and technology more difficult to manage at scale.
The good news is that breast imaging professionals are already realizing measurable value from AI. In a recent survey of Society of Breast Imaging members, 55.6% of breast radiologists reported using AI-assisted computer-aided detection tools in clinical practice, and 73.2% reported that AI positively impacts workflow efficiency.2 As AI adoption continues to grow, this raises an important question: are we just investing in tools, or are we building a system that actually works?
More healthcare leaders are stepping back and realizing the issue isn’t a lack of technology; it’s how that technology is chosen, connected, and integrated. The shift isn’t about buying more; it’s about buying smarter.
Here are 10 actionable strategies to help you build a high-performing, future-ready breast screening ecosystem.
When imaging hardware comes with embedded tools, it can feel like an easy decision. Everything arrives together, and it’s “seamless”.
But over time, these bundles can become restrictive, leading to:
The reality is that, while convenience at the point of purchase feels like a no-brainer, it can actually create complexity and limitations down the line.
Broad AI platforms promise to cover multiple use cases under one umbrella, which can be appealing from an efficiency and procurement perspective. But breast screening is a highly specialized clinical pathway with unique workflows, stakeholders, and decision points.
And that’s where the real opportunity lies. Evidence increasingly suggests that specialized breast imaging AI can deliver meaningful operational and clinical benefits when implemented thoughtfully. For example, a recent digital breast tomosynthesis (DBT) study showed that breast-specific AI reduced interpretation time by nearly six seconds per case, while improving radiologist sensitivity without sacrificing specificity.3
These gains reflect the unique challenges of breast imaging, where increasing image volumes, subtle findings, and screening-scale workloads require tools purpose-built for breast imaging rather than generic enterprise AI platforms designed to serve multiple radiology specialties.
The organizations that are seeing meaningful improvements across their breast screening workflows aren’t starting with vendors or features; they’re starting with one simple question:
Where are we losing time, consistency, or confidence today?
Common answers include:
So, define the problem first, then procure the technology that solves it. When you start with the outcomes you need to achieve, it becomes easier to identify the solutions that will deliver meaningful value.
For years, detection has been the focal point of breast screening investment, and for good reason. A prospective study in a single-read setting including over 24,000 women, found that when radiologists used AI as a “second pair of eyes”, there was a 13.8% higher cancer detection rate without increasing the recall rate, compared to when radiologists didn’t utilize AI .4
But detection is only one part of the broader breast cancer care equation. A high-performing program now spans the full continuum, from identifying risk early, to understanding breast density, to ensuring image quality, to tracking follow-up and outcomes.
When one piece is missing or disconnected, the overall program becomes less effective. Today, AI solutions exist that can support the entire breast cancer care pathway, empowering organizations to take a more connected, strategic approach.
Rather than layering on more tools as they need them, leading programs are designing connected workflows from the start.
That means cancer risk evaluation isn’t something separate; it’s built into the screening process. Imaging, interpretation, quality checks, and follow-up aren’t isolated steps; they’re part of a continuous, coordinated pathway.
The difference is subtle but powerful. When systems are connected, variability decreases. Teams spend less time navigating tools and more time focusing on patient care – giving their patients a more consistent, streamlined journey.
The importance of implementing technology that integrates naturally within existing clinical and operational workflows cannot be overstated. In the Society of Breast Imaging survey, 62.0% of respondents identified software integration as a significant barrier to AI adoption.2 When AI tools fail to communicate with existing systems, organizations often experience fragmented workflows, duplicate work, and reduced clinician confidence.
As more AI solutions enter the market, it’s tempting to focus on functionality. But clinical confidence isn’t built on features alone; it’s built on evidence.
That means seeking solutions that are validated through rigorous peer-reviewed research and tested across large, diverse patient populations5 is key to success. Adoption depends on trust, and trust is earned through proof, not promises.
Clinical validation should also extend beyond just diagnostic accuracy. Organizations should also look for evidence among leading programs globally that AI can improve operational performance in real-world environments.
Another theme that emerges across leading organizations is the importance of interoperability.
Most imaging environments aren’t uniform – they involve multiple vendors, systems, and platforms. Any solution that assumes otherwise will struggle to scale.
The ability to integrate across that complexity isn’t just a technical requirement; it’s what allows programs to evolve over time without being locked into a single path.
Look for solutions that:
Ask prospective vendors about their track record implementing AI across diverse healthcare settings over time.
Many systems generate data, but few actually turn that data into something actionable.
The programs advancing the fastest are those with clear visibility into their entire screening pathway, including where quality varies, where workflows slow down, and where patients fall through the cracks.
That visibility changes decision-making. It allows teams to move from reactive adjustments to continuous improvement. This is particularly important as imaging volumes continue to rise and staffing challenges persist. In the same Society of Breast Imaging survey, 46.3% of respondents believed AI could help address radiologist shortages, while 47.0% reported that AI has the potential to reduce burnout.2
Organizations that measure and optimize program-level outcomes are often better positioned to realize these benefits.
A strong AI-enabled system in breast screening should support:
Technology in the healthcare AI space is evolving rapidly, and a solution that meets today's needs may not be enough tomorrow. A quality vendor partner has a comprehensive future innovation roadmap that guides their AI evolution. Selecting a vendor with a demonstrated commitment to innovation can help ensure your investment today continues to deliver value in the future as breast imaging technology evolves.
That’s why more organizations are moving toward models that support frequent updates and innovation, rather than one-time purchases that quickly become outdated.
A smart AI investment strategy considers:
It’s not just about keeping up; it’s about staying ahead.
One of the most overlooked factors in all of this is the role of the vendor.
The most successful implementations don’t come from products alone; they come from partnerships that provide:
Even the best technology won't deliver value if it isn't fully embedded into how people work or set up to scale and evolve over time.
Leading providers are shifting from buying isolated features to implementing end-to-end screening pathways – Lexington Clinic is one of those providers. They are a large multispecialty group that implemented a single comprehensive breast AI ecosystem from Lunit covering:
The impact?
Start by asking three questions:
Next step: Build a purpose-designed ecosystem tailored to mammography, rather than relying on bolt-on solutions.
Healthcare leaders who prioritize integration, focus on measurable outcomes, and design around the full screening pathway will deliver more consistent, higher-quality care while staying adaptable for whatever comes next.
References
Becky Weber is the Executive Vice President of Sales at Lunit International, a global healthcare AI company focused on improving cancer detection and treatment through advanced artificial intelligence solutions. In her role, she leads sales strategy and business growth initiatives, helping expand the adoption of Lunit’s AI-powered cancer screening technologies across healthcare organizations worldwide.
Becky brings more than 15 years of experience in healthcare technology and leadership. She has long been passionate about the intersection of healthcare and technology, driven by a belief that innovation can improve patient outcomes and transform the way care is delivered. Her commitment to advancing cancer screening is also deeply personal, shaped by her own experience with breast cancer and the importance of early detection.
Prior to joining Lunit in 2024, Becky held leadership positions at Apple and Epic, where she developed expertise in healthcare technology, product strategy, software implementation, operations, and customer success.
Known for her customer-focused approach and deep understanding of healthcare systems, Becky has built her career helping healthcare organizations harness technology to improve outcomes, increase access to care, and drive meaningful innovation. She graduated from Indiana University’s Kelley School of Business.
