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Healthcare · head to head

Annalise.ai vs Lunit

Annalise.ai logo

Annalise.ai

Healthcare

Multi-finding chest X-ray and head CT triage AI with FDA clearance on named findings, not on the whole model

From
On request
Rated
-
Full reviewWebsite
Lunit logo

Lunit

Healthcare

FDA cleared and CE marked AI for mammography, tomosynthesis and chest radiography

From
On request
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Annalise.ai the United States cleared indication covers roughly ten findings for triage and notification, far fewer than the model reports, so a buyer who evaluates the full multi-finding output and then contracts in the United States gets a much narrower cleared use.; Lunit the regulatory permission differs by product and by region, and INSIGHT Risk has been filed with the FDA but not cleared, so a buyer who reads a corporate press release rather than the specific clearance for their product and jurisdiction will plan a deployment they cannot legally run.
  • They diverge on capability: Annalise.ai covers Annalise Enterprise CXR, Lunit covers Lunit INSIGHT MMG.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Annalise.ai and Lunit actually diverge.

Attributes where Annalise.ai and Lunit differ
AttributeAnnalise.aiLunit
PlatformsWeb, Cloud, On-premiseWeb

Identical on both: starting price (On request), pricing model (quote), free tier (No), user rating (Not yet rated), category (Healthcare).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Annalise.ai

  • Annalise Enterprise CXR
  • Annalise Enterprise CTB
  • Triage and notification
  • Worklist reprioritisation
  • Localisation overlays
  • Confidence output
  • PACS-native deployment
  • On-premise or cloud inference

Only in Lunit

  • Lunit INSIGHT MMG
  • Lunit INSIGHT DBT
  • Lunit INSIGHT CXR
  • Volpara density assessment
  • Volpara RiskPathways
  • Volpara dose tracking
  • PACS and mammography workstation integration

What people use each for

The jobs each tool is most often brought in to do.

Annalise.ai

  • An emergency department wanting pneumothorax and intracranial haemorrhage pushed to the front of the reading queue overnight when cover is thinnestnot Lunit
  • A teleradiology provider covering many small hospitals that needs consistent triage before a human readsnot Lunit
  • A screening programme in Europe where the broader CE marked decision support indication, rather than the narrow United States triage clearance, is what is being deployednot Lunit
  • A radiology department building an AI governance programme that wants one vendor covering both chest radiograph and head CT rather than contracting two separate algorithm suppliersnot Lunit

Lunit

  • A national breast screening programme evaluating AI as a second reader to reduce double reading workload where two human readers are currently mandatednot Annalise.ai
  • A breast centre that moved to tomosynthesis and now has reading times it cannot staff, wanting detection support on DBT specificallynot Annalise.ai
  • An American breast imaging centre needing automated volumetric density assessment to meet patient density notification requirements, which is what Volpara is bought fornot Annalise.ai
  • A tuberculosis or lung screening programme in a high volume, radiologist scarce setting using chest radiography triage to prioritise filmsnot Annalise.ai

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Annalise.ai

  • The United States cleared indication covers roughly ten findings for triage and notification, far fewer than the model reports, so a buyer who evaluates the full multi-finding output and then contracts in the United States gets a much narrower cleared use.
  • Triage AI reprioritises a worklist but does not change clinical decisions on its own, so departments that do not redesign how urgent studies are picked up see no measurable improvement in time to treatment.
  • Published validation is largely reader study evidence on detection of individual findings; prospective evidence of shorter time to treatment in routine emergency care is thin.
  • The Annalise brand is being migrated under the Harrison.ai name for the critical care offering, which creates ambiguity in multi-year contracts about what product name is actually being supported.
  • A high finding count raises the alert burden, and departments report that low prevalence findings generate false positives that erode radiologist trust unless the alerting thresholds are tuned locally.

Lunit

  • The regulatory permission differs by product and by region, and INSIGHT Risk has been filed with the FDA but not cleared, so a buyer who reads a corporate press release rather than the specific clearance for their product and jurisdiction will plan a deployment they cannot legally run.
  • Detection AI changes recall rates in screening, and the downstream cost of additional diagnostic workup lands on the programme rather than on the software budget, which is the cost most screening business cases omit.
  • Performance is sensitive to the mammography vendor and acquisition protocol, so published results from a Hologic estate do not transfer to a GE or Siemens estate without local validation the buyer must fund.
  • MMG, DBT and CXR are licensed separately and Volpara modules are separate again, so a breast centre wanting detection, density and risk pathways is assembling four line items rather than buying one product.
  • Using AI as a replacement for one of two human readers is a workforce and liability decision that regulators and professional bodies treat differently in each country, so the saving that justifies the purchase may not be permitted where you operate.

Pricing, plan by plan

Annalise.ai

On request
  • Annalise Enterprise$undefined/year
    • Priced per study analysed or as an annual volume subscription
    • CXR and CTB licensed separately
    • Cleared finding set differs by market, which changes what you are buying

Lunit

On request
  • Lunit INSIGHT$undefined/year
    • Priced per study or per annual examination volume by product
    • MMG, DBT and CXR licensed separately
    • Volpara density and RiskPathways modules quoted separately

Which should you pick?

Choose Annalise.ai if

  • You need annalise enterprise cxr.
  • You work on Web, Cloud, On-premise.
  • You also want annalise enterprise ctb.
Annalise.ai review

Choose Lunit if

  • You need lunit insight mmg.
  • You also want lunit insight dbt.
Lunit review

Questions people ask

Is Annalise.ai or Lunit better?
Neither clearly leads. Annalise.ai starts at On request and Lunit at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Annalise.ai or Lunit?
Annalise.ai starts at On request and Lunit at On request.
Does Annalise.ai or Lunit run on more platforms?
Annalise.ai runs on Web, Cloud, On-premise. Lunit runs on Web.
What is Annalise.ai best used for?
Annalise.ai is most often used for an emergency department wanting pneumothorax and intracranial haemorrhage pushed to the front of the reading queue overnight when cover is thinnest, a teleradiology provider covering many small hospitals that needs consistent triage before a human reads, a screening programme in europe where the broader ce marked decision support indication, rather than the narrow united states triage clearance, is what is being deployed, a radiology department building an ai governance programme that wants one vendor covering both chest radiograph and head ct rather than contracting two separate algorithm suppliers. Of those, an emergency department wanting pneumothorax and intracranial haemorrhage pushed to the front of the reading queue overnight when cover is thinnest and a teleradiology provider covering many small hospitals that needs consistent triage before a human reads are not what Lunit is typically brought in for.
What can Annalise.ai do that Lunit cannot?
Annalise.ai covers Annalise Enterprise CXR, Annalise Enterprise CTB, Triage and notification, Worklist reprioritisation. Lunit covers Lunit INSIGHT MMG, Lunit INSIGHT DBT, Lunit INSIGHT CXR, Volpara density assessment.

Answered from the vendors’ own pages

Annalise.ai: How many findings are FDA cleared?

Around ten across chest X-ray and head CT, for triage and notification only. The model reports far more findings than that; the rest are not cleared in the United States.

Lunit: Is Lunit FDA cleared?

Yes for specific products. INSIGHT MMG, INSIGHT DBT and INSIGHT CXR Triage hold FDA 510(k) clearances. INSIGHT Risk, the five year risk prediction model, has been filed but was not cleared at the time of writing.

Annalise.ai: Is the European position different?

Yes. CE marking under the medical device regulation covers a broader decision support indication than the United States triage and notification clearances, so the same software is a different regulatory product depending on where you deploy it.

Lunit: Can Lunit replace a second human reader in screening?

That depends entirely on your jurisdiction and professional body guidance, not on the software. The CE mark permits clinical use; it does not authorise a workforce model.

Annalise.ai: Does it diagnose?

No. In the United States it is a triage and notification device: it flags studies for earlier review, and the radiologist makes the diagnosis.

Lunit: What did the Volpara acquisition add?

Volumetric breast density measurement, dose tracking and RiskPathways high risk programme management, which are the things American breast centres buy for density notification compliance rather than for detection.

Annalise.ai: Is Annalise.ai becoming Harrison.ai?

The critical care offering is now marketed under the Harrison.ai name. Ask the vendor to name the contracted product explicitly before signing a multi-year deal.

Lunit: Is pricing published?

No. It is quoted per study or per annual volume, by product, with on premises and cloud priced differently.

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