What's the best AI for breast cancer detection (for your screening program)?

Click here to skip to the end and request your copy of our vendor comparison checklist, created to help support you and your organization’s decision-making process.

Breast imaging has emerged as one of the clearest areas where AI can move from promise to practical value. As the researcher Dr. Eric Topol recently noted in his newsletter, “mammography with AI support has emerged as the most rigorously studied of all possible indications. It is a standout for demonstrating superhuman performance—“digital eyes”— that see things which humans can’t.”

But for many breast imaging leaders, the decision is not simply “Should we adopt AI?” The better question is: Which AI vendor can help me improve detection, optimize our workflow, earn the trust of my radiologists, and scale safely across our organization?

Here are five criteria every radiology leader should consider when comparing breast cancer detection AI (“Breast AI”) tools.

Vendor agnostic

A breast imaging AI solution should strengthen your clinical program without forcing your organization into unnecessary vendor dependency.

Many health systems, screening programs and imaging networks operate mixed environments: different mammography systems, PACS platforms, reporting tools, acquisition protocols, and site-level workflows. A tool that performs well only within a narrow technical ecosystem may limit future flexibility, complicate expansion, and create friction when hardware or software changes.

Radiology leaders should look for AI that can work across modalities, manufacturers, and deployment environments. Vendor-agnostic AI is not just an IT preference; it is an operational strategy. It gives leaders more freedom to standardize quality across sites, expand programs over time, and avoid building a breast imaging strategy around a single equipment or software relationship.

The practical questions are straightforward: Has the tool been validated across the systems we use today? Can it support both FFDM and DBT (digital breast tomosynthesis) where relevant? Will it continue to work if we change PACS, acquire a new site, or update our acquisition hardware?

The right AI partner should fit your ecosystem, not force your ecosystem to fit the AI.

Available within your workflow

The best AI is the AI radiologists can (and will) actually use.

For breast imaging teams, workflow matters nearly as much as algorithmic performance. Radiologists are already managing high screening volumes, diagnostic complexity, callbacks, comparison studies, reporting requirements, and patient expectations. Adding a separate workstation, extra login, disconnected viewer, or manual upload process can quickly turn a promising tool into an abandoned one.

AI should appear where decisions are already being made: in the reading environment, aligned with PACS, worklist, reporting, and established hanging protocols. Results should be timely, clear, and actionable. Marks, scores, case prioritization, density information, or second-reader support should be presented in a way that helps the radiologist maintain focus rather than introducing distraction.

This is also where governance becomes essential. The ACR-SIIM Practice Parameter for Imaging AI emphasizes that practices need processes for tool selection, pre-deployment evaluation, ongoing monitoring, privacy protection, and continuous quality improvement. Your goal is to ensure long-term success rather than a one-time installation. (American College of Radiology)

A tool that lives outside the workflow may become optional. A tool that lives inside the workflow can become transformational.

Backed by research

Breast AI should be supported by evidence that goes beyond internal validation, marketing claims, or retrospective performance on curated datasets.

This standard of evidence should include peer-reviewed studies, external validation, prospective or real-world deployment data, and transparency around performance across patient populations, breast density, cancer types, imaging systems, and clinical settings. FDA authorization may be an important starting point in the United States (as is CE clearance in the EU), but it should not be the endpoint of evaluation.

As noted by Dr. Topol, the research base for mammography AI is strong and becoming stronger. In the MASAI randomised controlled trial, the use of Transpara led to higher sensitivity with the same specificity, a non-inferior interval cancer rate, and reduced screen-reading workload. Two additional RCTs are underway, one in the United States and one in Norway. New research has explored cost-effectiveness, standalone screening for normal exams, triage for supplemental screening, and much more.

For your decision-making process, two things are clear: evidence should be specific and it should be comprehensive. Ask whether the research reflects your modality mix, patient population, screening cadence, staffing model, and clinical objectives. The strongest AI tools are not only accurate in a paper: they are measurable, monitorable, and defensible in practice.

Trusted at scale, by the best

Evidence also is not limited to studies and publications: trust in AI is earned through performance in real clinical environments, within organizations or programs like yours.

Radiology leaders should ask where the tool is being used, how broadly it has been deployed, and what kind of organizations are willing to rely on it. But scale should be interpreted carefully. Large deployment alone is not enough. The more important question is whether the AI has proven it can operate reliably across varied clinical environments while maintaining quality, safety, and user confidence.

The “best” institutions and imaging organizations tend to evaluate AI with discipline. They involve radiologists, IT, compliance, operations, quality leaders, and executive stakeholders. They run local validation. They monitor drift. They define escalation pathways. They track whether AI is improving the metrics that matter: cancer detection, recall, positive predictive value (PPV), reading efficiency, health equity, and patient outcomes.

That kind of discipline is becoming standard. The American College of Radiology has introduced resources such as Assess-AI and ARCH-AI to support responsible imaging AI implementation, including performance monitoring and quality assurance across clinical use cases. (American College of Radiology)

For a radiology leader, trust at scale means confidence that the tool can move from pilot to a sustainable enterprise program. It means the AI is governed, audited, supported, and improved. It means your radiologists understand what the AI is doing, administrators understand the return on investment, and your population benefits from a more effective and more consistent screening experience.

Performance and expertise

“Highest performance” may not be represented by a single headline metric. However, that can be a good place to start.

Ask your vendors about their exam-based AUCs as well as sensitivity and specificity metrics. Are these broken down by ethnicity, modality, and more? Some vendors may offer a mix of these metrics within their regulatory filings (see this example for Transpara – note: links to PDF download from FDA)

Yet in breast cancer detection, performance is multidimensional. A tool that increases recalls without proportional clinical benefit may create unnecessary downstream burden. A tool with strong standalone accuracy may still underperform when inserted into a real clinical workflow. A tool that seems to have high overall metrics may still require deeper evaluation across dense breasts, age groups, racial and ethnic subpopulations, cancer subtypes, and imaging devices.

Radiology leaders should evaluate performance through a balanced scorecard. Important measures include cancer detection rate, sensitivity, specificity, recall rate, positive predictive value, interval cancer rate, reading time, workload reduction, radiologist acceptance, and performance consistency across sites and subgroups.

The strongest AI tools should help radiology teams detect more clinically meaningful cancers earlier while preserving or improving workflow efficiency. The leadership question is not, “Which AI has the highest score?” It is, “Which AI delivers the best clinical, operational, and financial performance for our organization—and can we prove it over time?”

Your leadership decision

Choosing breast cancer detection AI is not a technology purchase. It is a clinical transformation decision.

The right tool should be vendor agnostic, embedded in workflow, supported by strong research, trusted at scale, and able to demonstrate high performance on the outcomes that matter most. It should make radiologists more confident, operations more efficient, and screening programs more consistent. Most importantly, it should help patients receive earlier, more reliable detection without adding unnecessary complexity to care.

For radiology leaders, the path forward is not to adopt AI quickly or cautiously. It is to adopt AI intelligently – the vendor comparison checklist we provide below can help you do just that.