# Lunit vs MD.ai

Lunit records 29,181 monthly visits with -12.8% month-over-month growth and Contact for Pricing pricing. MD.ai records 6,650 monthly visits with -11.2% growth and Contact for Pricing pricing.

| Metric | [Lunit](https://openaitools.app/tool/lunit) | [MD.ai](https://openaitools.app/tool/md-ai) |
| :--- | :--- | :--- |
| Website URL | https://www.lunit.io/en | https://md.ai |
| Categories | AI Healthcare, AI Medical Diagnosis, AI Image Recognition | AI Models, AI Healthcare, AI Report Generator, Large Language Models (LLMs) |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 29,181 | 6,650 |
| MoM Growth Rate | -12.8% | -11.2% |
| Primary Market | South Korea | United States |
| Direct Visits | 8,314 | 2,596 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Lunit

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.

**Pricing & Plans:** Contact for pricing. Lunit sells enterprise software and platform licenses to hospitals, screening programs, and biopharma partners, so commercial terms and deployment costs are quoted directly through its sales team. No public price list is offered.

- **Lunit INSIGHT MMG and DBT:** Deep-learning analysis of 2D and 3D mammography that marks suspicious lesions to help radiologists detect breast cancer earlier and reduce missed findings.
- **Lunit INSIGHT CXR:** Chest X-ray analysis that highlights potential abnormalities such as nodules and other findings, supporting faster triage in high-volume radiology workflows.
- **Lunit SCOPE IO:** Tumor microenvironment analysis that quantifies tumor-infiltrating lymphocytes and immune phenotypes from pathology slides, informing immunotherapy research.
- **Lunit SCOPE IHC Suite:** AI-assisted immunohistochemistry quantification covering HER2, PD-L1, and other markers, including uIHC and a gene-expression prediction tool for standardized scoring.
- **Screening analytics and workflow tools:** Live mammography reporting, risk assessment, risk pathways, quality management, and patient-facing tools that embed AI into the wider screening program.

**Strengths:** Clinically validated radiology AI with documented regulatory clearances and published evidence; Covers two ends of the cancer pathway — screening detection and precision oncology biomarkers — in one vendor relationship; Wide real-world adoption across many countries, which means integrations and workflow lessons already exist; Strong research output and biopharma partnerships support credibility for trial and CDx work

[Profile (Markdown)](https://openaitools.app/tool/lunit.md)

## MD.ai

MD.ai's Reporting product is built to remove friction from the reading workflow rather than replace the radiologist. It selects report templates automatically, maps dictated key findings into structured fields, drafts impressions from what was dictated, proofreads the result, and generates billing codes to cut administrative work. It also supports contextual AI chat, multilingual output and patient-friendly audio summaries, and connects to EHR, HIS and RIS environments through HL7 and DICOM integration. Because it runs in the browser it works across desktop, laptop, tablet and mobile with multi-device sync.

The Annotator product targets dataset creation and model development. It offers native DICOM support, an FDA 510(k)-cleared viewer, AI-assisted annotation and seamless scaling, with PHI detection and de-identification tooling plus developer APIs. According to the company, the platform has helped thousands of physicians and their collaborators build large, high-quality labeled datasets, validate models and assemble AI-driven clinical workflows.

MD.ai markets to hospital radiology departments, imaging AI startups and research groups. Commercial terms are not published on the public site: interested teams are directed to request a demo or contact the company directly, which is typical for clinical software that involves security review, integration scoping and volume-based licensing.

**Pricing & Plans:** Pricing is not published publicly; MD.ai asks prospective customers to request a demo or contact the team, and commercial terms are arranged directly. This is typical for clinical imaging software, where licensing depends on deployment scope, integrations and institution size.

- **LLM-assisted reporting:** Automatic template selection, dictation-to-findings mapping, impression generation and proofreading inside a radiology reporting workflow.
- **Automated administrative tasks:** Generates billing codes from the report and produces patient-friendly audio summaries for clearer communication and education.
- **EHR/HIS/RIS integration:** HL7 and DICOM integration connects the reporting tool to existing clinical systems, with AI-driven or traditional reporting modes available.
- **DICOM-native annotation:** Build labeled imaging datasets with native DICOM support, AI-assisted annotation, an FDA 510(k)-cleared viewer and elastic scaling.
- **Privacy and developer tooling:** PHI detection and de-identification for dataset work, plus developer APIs for teams deploying and validating imaging models.

**Strengths:** Purpose-built for radiology rather than a generic LLM wrapper, with DICOM-native data handling; Reporting and annotation live on one platform, so datasets and deployment workflows stay connected; An FDA 510(k)-cleared viewer and PHI de-identification tooling address real clinical compliance needs; Runs in a browser across devices with multi-device sync and multilingual support

**Limitations:** No public pricing, so evaluation requires contacting sales and a demo cycle; Full value depends on HL7/DICOM integration with hospital systems, which adds deployment effort; The platform is aimed at clinical and research organisations, so individual users are unlikely to get a lightweight self-serve option

[Profile (Markdown)](https://openaitools.app/tool/md-ai.md)

## Frequently Asked Questions

### How do Lunit and MD.ai compare in monthly traffic?

Lunit has 29,181 monthly visits, while MD.ai has 6,650. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Lunit has a month-over-month growth rate of -12.8%; MD.ai has -11.2%.

### How does pricing compare?

Lunit: Contact for Pricing. Contact for pricing. Lunit sells enterprise software and platform licenses to hospitals, screening programs, and biopharma partners, so commercial terms and deployment costs are quoted directly through its sales team. No public price list is offered. MD.ai: Contact for Pricing. Pricing is not published publicly; MD.ai asks prospective customers to request a demo or contact the team, and commercial terms are arranged directly. This is typical for clinical imaging software, where licensing depends on deployment scope, integrations and institution size.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | +5.1% | Free | Open-source model and dataset hub with one-click online demos | [.md](https://openaitools.app/tool/modelscope-community.md) |
| 2 | [Dr.Oracle](https://openaitools.app/tool/dr-oracle) | 723,847 | -1.2% | Free Trial | Guideline-directed medical answers with citations, built for clinicians | [.md](https://openaitools.app/tool/dr-oracle.md) |
| 3 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Human-validated training data and evaluation services for frontier AI models | [.md](https://openaitools.app/tool/appen-com.md) |
| 4 | [ScanSkinAI](https://openaitools.app/tool/scanskinai) | 265,770 | -10.9% | Freemium, Paid | Free AI skin and mole scanning with optional dermatologist review | [.md](https://openaitools.app/tool/scanskinai.md) |
| 5 | [Overjet](https://openaitools.app/tool/overjet-ai) | 256,150 | +12.6% | Contact for Pricing | FDA-cleared dental AI for diagnostics, imaging, and insurance workflow | [.md](https://openaitools.app/tool/overjet-ai.md) |
| 6 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | The multimodal data layer for computer vision and physical AI teams | [.md](https://openaitools.app/tool/encord-com.md) |
| 7 | [Pearl](https://openaitools.app/tool/hellopearl-com) | 221,155 | -7.6% | Contact for Pricing | FDA-cleared dental AI for sharper diagnosis and a smoother front office | [.md](https://openaitools.app/tool/hellopearl-com.md) |
| 8 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Label any data. Evaluate any AI. | [.md](https://openaitools.app/tool/label-studio.md) |
| 9 | [0xmd](https://openaitools.app/tool/0xmd-com) | 128,262 | -2.2% | Freemium | Generative AI for medical and everyday health questions | [.md](https://openaitools.app/tool/0xmd-com.md) |
| 10 | [CT Read](https://openaitools.app/tool/ct-read) | 103,157 | -5.8% | Paid | AI analysis that turns X-rays, CT scans, MRI and ultrasound into plain-language reports | [.md](https://openaitools.app/tool/ct-read.md) |

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Source: https://openaitools.app/compare/lunit-vs-md-ai

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