Hospitals do not have an AI model problem. They have an integration problem: three vendors, three portals, three worklists, and nothing that reasons across studies. BonaLabs is the layer that absorbs that.
Standard DICOMweb, so any PACS or modality gateway pushes studies with no custom integration. De-identified on receipt, with UIDs remapped rather than deleted so priors stay linkable and dates shifted rather than zeroed so interval change survives.
Narrow detectors run first, in parallel. Agentic workflows then reason over their output, the patient's priors, and the clinical indication, answering questions a single-study model structurally cannot. Every step is recorded in an auditable trace.
Findings from every model fused into a single ranked queue. The score is explicit, not learned, so a radiologist can see exactly why a study sits where it does, because a ranking they cannot interrogate is one they will correctly ignore.
Images never leave your network. BonaLab deploys inside your firewall, connecting directly to your PACS/VNA.
Every ranking is explained, and a reader can override any AI severity. Those overrides become the ground truth for how a model performs in your population.
Any model, any cloud, or none. One layer over every vendor you use, with storage, queueing and inference behind swappable adapters, and no cloud SDKs.
Every study takes the same path, and every decision along it is recorded.
DICOMweb from any PACS. De-identified on receipt, priors kept linkable
Match models by modality, body part, age and indication, with rejections logged
Narrow models run in parallel: your own, or any vendor's
Agentic workflows correlate detections against priors and the indication
Findings fused into one worklist, with the score explained
Step 04: what a narrow model cannot do
A detector sees one study. It cannot tell you the nodule grew, that the finding is unrelated to why the scan was ordered, that the haemorrhage it flagged is chronic and already known, or that the study lacks the sequence needed to answer the question.
Agentic workflows answer exactly those. They call the detectors as tools, read the patient's priors and prior reports, and must cite the evidence behind every finding they record. Each step, whether reasoning, tool call or result, is written to an auditable trace a radiologist can open.
Longitudinal comparison
What changed since the prior?
Incidental findings
What did we find that nobody asked about?
Critical corroboration
Does this alert justify an interruption?
Protocol & quality
Is this study even adequate?
The result: one ranked worklist across every model you run, with the reasoning behind each position visible to the radiologist reading it.
Prioritize critical findings. Auto-flag pneumothorax, cardiomegaly, and other urgent conditions so your radiologists see the most critical cases first.
Trained on your imaging archive. Calibrated to your workflow.
Catch what gets missed. AI second-read for nodules, masses, and other incidental findings that may not be the primary indication.
Reduces miss rates. Improves patient outcomes.
Automated peer review. Compare AI predictions against final reports to identify discrepancies and learning opportunities.
Continuous improvement for your department.
Accelerate your research. Build labeled datasets at scale for retrospective studies, clinical trials, and academic publications.
IRB-ready annotation workflows.