AI Agents That Augment Diagnostic Precision

No — and that is explicitly not our approach. AI agents augment radiologists by pre-reading studies, flagging critical findings, and prioritizing worklists. The radiologist always makes the final interpretation. Our AI reduces workload and catches findings that might be missed during high-volume reading sessions.

Accuracy depends on the modality and pathology. For specific use cases like mammography screening, lung nodule detection, and fracture identification, AI models achieve sensitivity and specificity comparable to or exceeding expert radiologists — but always as a second reader, never as a standalone diagnostic tool.

Our AI agents work across CT, MRI, X-ray, mammography, ultrasound, and whole-slide imaging (digital pathology). We deploy modality-specific models optimized for each imaging type, with the ability to correlate findings across multimodal studies.

AI agents analyze whole-slide images at cellular resolution: identifying mitotic figures, quantifying biomarkers (Ki-67, PD-L1, HER2), detecting tumor margins, and grading specimens. This augments pathologists' diagnostic accuracy and reduces turnaround time for complex cases by 30-50%.

We implement AI as clinical decision support — not as an autonomous diagnostic device. This means clinician oversight is mandatory for all findings. For deployments requiring CE marking (MDR) or FDA clearance, we design systems aligned with those regulatory pathways and provide the documentation needed for submission.

We implement rigorous bias testing across patient demographics (age, sex, ethnicity), imaging equipment types, and clinical settings. Models are validated on diverse datasets and continuously monitored for performance drift across population subgroups. Bias reports are included in every model deployment.

Yes. AI agents triage incoming studies and flag critical findings (stroke, PE, pneumothorax, fractures) within seconds of image acquisition. This enables radiologists to read urgent cases first, reducing time-to-diagnosis for critical conditions from hours to minutes.

We connect via DICOM and HL7/FHIR interfaces to major PACS platforms (Sectra, Agfa, Philips, GE). AI pre-reads are delivered directly into the radiologist's reading workflow — no separate application needed. Results appear as structured findings in the standard reporting interface.

Typical outcomes: 40-60% reduction in diagnostic turnaround time, 15-25% improvement in critical finding detection rates, 30% reduction in repeat studies, and measurable increase in radiologist throughput without sacrificing quality.

Yes. AI agents analyze tumor characteristics, track longitudinal changes, quantify treatment response (RECIST criteria), and correlate imaging with genomic and clinical data. This supports both clinical treatment decisions and research protocol adherence.