Medical AI for earlier cancer detection and precision oncology
Estimated monthly website visits
Similarweb estimates for this domain, not active users. Only available observation months are shown; collection date is separate from the traffic period.
Collected Nov 11, 2026
This is our editors' assessment out of 5, not an average of user ratings. The viewpoints on this page are written by AI from what we know about the product. They are not customer reviews.
According to OpenAITools.app telemetry verified on 2026-11-11, Lunit (lunit.io) is an AI platform indexed under AI Healthcare generating 29.18K monthly visits with a month-over-month traffic delta of -4.3K (-12.8%). Its primary geographic market is South Korea (58.4% traffic share), and it operates on a Contact for Pricing pricing model. Top competing alternatives by monthly volume include iCAD (6.1K visits/mo), Oatmeal Health (40.26K visits/mo), RapidAI (20.17K visits/mo).
Incredible accuracy in spotting early stage nodules on chest X-rays.
Speeds up radiology reporting significantly during busy shifts.
Lunit INSIGHT is a game changer for mammography screening.
Integration with PACS workflows is robust and reliable.
AI-synthesized perspectives grounded in live site inspection, pricing structure, and category benchmarks — presented as editorial viewpoints rather than individual customer testimonials.
Deep-learning analysis of 2D and 3D mammography that marks suspicious lesions to help radiologists detect breast cancer earlier and reduce missed findings.
Chest X-ray analysis that highlights potential abnormalities such as nodules and other findings, supporting faster triage in high-volume radiology workflows.
Tumor microenvironment analysis that quantifies tumor-infiltrating lymphocytes and immune phenotypes from pathology slides, informing immunotherapy research.
AI-assisted immunohistochemistry quantification covering HER2, PD-L1, and other markers, including uIHC and a gene-expression prediction tool for standardized scoring.
Live mammography reporting, risk assessment, risk pathways, quality management, and patient-facing tools that embed AI into the wider screening program.
Lunit is a medical AI company headquartered in Seoul, South Korea, spun out of deep-learning research and focused squarely on oncology. Rather than chasing general-purpose healthcare AI, it has built two connected product families: one that helps clinicians find cancer earlier in imaging, and one that extracts quantitative biology from tissue to guide treatment and drug development. Its technology is used across a large international footprint spanning more than 10,000 customer sites in over 70 countries, with hundreds of peer-reviewed publications and industry partnerships behind the deployments.
On the screening side, the Lunit INSIGHT family analyzes mammograms and chest radiographs. Lunit INSIGHT MMG handles 2D mammography while Lunit INSIGHT DBT covers 3D breast tomosynthesis, both designed to flag suspicious regions so radiologists can prioritize the studies that need attention most. Lunit INSIGHT CXR does the same for chest X-rays. Around these detection engines Lunit has assembled program-level tooling: density assessment, risk assessment and risk pathways, live mammography reporting, quality management, analytics, and a patient hub, so a screening service can run AI as part of its routine rather than as an isolated experiment.
On the precision oncology side, the Lunit SCOPE portfolio analyzes pathology images. Lunit SCOPE IO quantifies the tumor microenvironment and immune cell infiltration, which matters for immunotherapy strategy, while the Lunit SCOPE IHC Suite supports immunohistochemistry quantification across markers such as HER2 and PD-L1, alongside a genotype prediction tool. These products are aimed at biopharma partners, clinical trial teams, and companion diagnostic development, where reproducible quantitative readouts are the bottleneck. Because Lunit sells to hospitals, screening programs, and pharmaceutical companies, pricing is quoted directly rather than published, and regulatory status differs by market.
Add a second, tireless read to every mammogram and chest X-ray so subtle cancers surface sooner and benign cases are handled with confidence.
Use AI-driven density, risk, and quality tools to segment screening populations and allocate follow-up resources where they matter most.
Quantify tumor-infiltrating lymphocytes and immune phenotypes from slides to test immunotherapy hypotheses and select patients for trials.
Standardize HER2, PD-L1, and other IHC scoring with AI-assisted quantification to support reproducible endpoints in regulated studies.
Side-by-side telemetry comparison of competing AI tools in the same primary category
| Rank | AI Tool | Monthly Visits | MoM Growth | Direct traffic share | Pricing | Head-to-Head |
|---|---|---|---|---|---|---|
| ● | Lunit | 29.18K | -12.8% | 28% direct | Contact for Pricing | Current |
| 1 | iCAD | 6.1K | +6.1% | 30% direct | Contact for Pricing | vs iCAD |
| 2 | Oatmeal Health | 40.26K | -44.9% | 85% direct | Contact for Pricing | vs Oatmeal Health |
| 3 | RapidAI | 20.17K | -27.9% | 45% direct | Contact for Pricing | vs RapidAI |
| 4 | CT Read | 103.16K | -5.8% | 19% direct | Paid | vs CT Read |
| 5 | Ibex Medical Analytics | 9.12K | -3% | 34% direct | Contact for Pricing | vs Ibex Medical Analytics |
| 6 | Pearl | 221.16K | -7.6% | 65% direct | Contact for Pricing | vs Pearl |
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