Explainable medical imaging AI

See why the model decided,
not just what it predicted.

Lumen reads chest X-rays, brain MRIs, retinal fundus photos and skin lesions — and returns a prediction, a calibrated confidence score, and a Grad-CAM map highlighting the exact region that drove the call. Evidence a clinician can actually review.

4
imaging modalities
23
findings supported
<9s
median read time
Live read STUDY · CXR-4471
CHEST · PA 512×512 GRAD-CAM ▸ ON W 2048 / L 600
lowhigh activation
Prediction
Pneumonia
High risk Moderate
Confidence
94.2%
Report Ready
Explainability check

Heat concentrated in the right lower lobe — consistent with the predicted finding.

Built for review workflows at
Meridian Health Northgate Radiology Vellore Eye Institute Atlas Research Lab
Capabilities

Four modalities, one explainable read

Each model is paired with its own explanation layer, so the evidence travels with the prediction.

CHEST X-RAY

Chest X-Ray

14 findings

Pneumonia, effusion, cardiomegaly, nodules and more — localised on the film, not just listed in text.

Brain MRI

Tumour classification across glioma, meningioma and pituitary, with slice-level localisation.

The difference

Every prediction ships with its proof

Grad-CAM overlays show the pixels that mattered. A confidence band tells you how sure the model is. Reviewers stay in control.

LOWACTIVATIONHIGH

Retinal Fundus

Diabetic retinopathy grading from healthy through proliferative, with vessel-level attention.

Skin Lesion

Benign vs. malignant triage on dermoscopic images.

Automated reports

A signed, printable PDF — findings, confidence and the overlay — in one click.

Held-out accuracy
96.4%

Macro-averaged across all four modalities on independent test sets.

How it works

From upload to defensible report in five steps

No DICOM gymnastics, no waiting room of tabs. Drop an image and follow the read as it unfolds.

STEP 1 · INGEST
01

Upload the image

Drag in a JPG, PNG or DICOM. We validate the modality and normalise it for the right model.

02

AI analysis

The matching model runs inference and produces class probabilities in seconds.

03

Disease detection

The leading finding, severity and a calibrated risk band are surfaced together.

04

Explainability

A Grad-CAM overlay reveals the region that drove the prediction, so you can agree or overrule.

05

Diagnostic report

Everything compiles into a clean, printable report ready for the patient record.

By the numbers

Measured, calibrated, and honest about uncertainty

0
Findings supported
0
Images processed
0
Mean accuracy
0
Reports generated

“The heatmap is the part that changed my mind. I'm not trusting a number in a box — I can see the model is looking where I'd look.”

AR Dr. Anjali Rao
Consultant Radiologist, Northgate

“We use it as a second reader for retinopathy screening. Throughput is up and nothing ships without the overlay attached.”

MS Dr. Marcus Silva · Ophthalmology Lead

“As a teaching tool it's unmatched — residents finally see what a model attends to versus a senior radiologist.”

PT Prof. Priya Thomas · Medical Education

Put an explanation behind every prediction

Create an account and run your first analysis in minutes. No installation, no DICOM server required.