Hospital oncology units
Prioritize suspicious cases, reduce review friction, and organize imaging signals for specialist review.
Clinical workflow intelligence
AI assistance for cancer teams that need faster, clearer review.
Lyfline helps cancer centres and research teams evaluate imaging-driven AI workflows for classification, progression prediction, and treatment planning support.
For cancer centres
Research pilots
Clinician-in-loop
Built for cancer centres
One assistive layer across specialist handoffs.
Lyfline is positioned for teams where oncology decisions depend on radiology, histopathology, clinical history, and research validation moving together.
Diagnostic networks
Support repeatable classification workflows across mammography, histopathology, ultrasound, and MRI pipelines.
Research institutions
Run controlled validation studies, compare model performance, and prepare datasets for clinically useful pilots.
Platform modules
From early signal to next-step planning.
Turn patient and EMR signals into review priority.
Assistive risk estimation for identifying which cases deserve faster specialist attention.
Subtype, grade, stage, and scan-aware findings.
Purpose-built imaging classification workflows for breast-cancer-focused clinical pilots.
Model likely progression across time and anatomy.
Progression support that turns classification context into forward-looking review signals.
Summarize clinical context for clinician-led planning.
Assistive recommendation layer for EMR, prescriptions, model outputs, and guideline-aware review.
Pilot outcomes
Measure operational value before broad deployment.
A Lyfline pilot is designed around measurable workflow improvement: turnaround time, review consistency, model sensitivity/specificity, escalation accuracy, and clinician acceptance.
Workflow advantage
A single view for imaging, risk, prediction, and planning support.
Lyfline gives cancer-care teams a structured operating layer for case intake, scan review, progression signals, and clinician-led planning decisions.
- Connect radiology, pathology, and oncology review into one workflow.
- Prioritize high-risk cases with consistent review context.
- Keep every AI-assisted output auditable for clinical governance.
Implementation path
A practical pilot in four steps.
Workflow selection
Choose one high-value cancer workflow, dataset boundary, and review team.
Data readiness
Prepare anonymized imaging and clinical fields with approval and access controls.
Model validation
Run classification and review outputs against clinician-labelled cases.
Operational review
Measure time saved, escalation accuracy, false positives, false negatives, and user acceptance.
Compliance-ready
Designed for clinician-in-loop deployment.
Assistive positioning
Lyfline is structured for clinician-led review, governance, and accountable decision support.
Controlled data handling
Pilots should use anonymized datasets, documented consent boundaries, access logs, and reviewable outputs.
Governed rollout
Deployment can be staged through validation reviews, operational checkpoints, and specialist approval.
For hospitals and research partners
Request pilot access.
Use Lyfline to evaluate a focused cancer workflow, prove clinical utility, and prepare the evidence base for responsible scale-up.