Deep learning architectures
For radiology, pathology, ophthalmology, and dermatology imaging.
Augmenting the trained eye - inside the workflow it already trusts.
Diagnostic imaging is one of the most studied areas in healthcare AI and one of the most demanding to deliver well.
The radiologists, pathologists, and diagnostic specialists who use these systems are not looking for a model that claims to replace them. They are looking for one that finds what might be missed, prioritises what they need to see first, and never disrupts the workflow they already trust.
Entiovi engineers medical imaging and diagnostic AI applications that integrate with existing PACS, RIS, LIS, and digital pathology environments, support standard formats including DICOM and whole-slide imaging, and operate within the accuracy, latency, and governance expectations of diagnostic teams.
Each system is built with validation, traceability, and regulatory readiness designed in from the first architectural decision - not retrofitted at the end of the engineering cycle. Model performance is reported in clinically meaningful terms, with sensitivity, specificity, and reader-level concordance treated as the primary evaluation surface.
For radiology, pathology, ophthalmology, and dermatology imaging.
Across CT, MRI, X-ray, ultrasound, and histopathology.
For time-critical findings such as acute stroke, pulmonary embolism, and intracranial haemorrhage.
Combining imaging with clinical history and laboratory context.
Via DICOM, HL7, and FHIR - with structured report generation and write-back.
Aligned to FDA, CE-MDR, and CDSCO regulatory pathways.
For thoracic abnormalities.
And breast cancer screening programmes.
In oncology imaging.
For ophthalmology networks.
Slide-level classification, region-of-interest detection, and biomarker quantification.
For time-sensitive conditions.
Faster reporting turnaround, improved consistency across readers, earlier flagging of critical and incidental findings, expanded screening reach in resource-constrained settings, and quantitative reproducibility for longitudinal and trial-grade work. The systems extend the capacity of diagnostic teams without taking the final read away from them.
Engagement begins with clinical use case scoping and a structured data and annotation assessment. From there, Entiovi designs the annotation workflow, develops and validates the model against site-specific and external data, integrates the system with diagnostic environments, and runs continuous evaluation against real-world reader performance. Regulatory documentation is produced alongside the system, not afterwards.
Imaging AI delivers value when it sits inside the workflow rather than beside it. Entiovi builds for that placement from the first design decision.
Talk to Entiovi about putting this discipline into production - from design and engineering through ongoing operation, with delivery from India and regional presence in your market.