Oxipit.aiAI-powered computer vision for medical imaging workflows.
Overview
Key features
- Automated chest X-ray abnormality detection
- Case prioritization and triage
- Radiology reporting assistance
- Quality assurance checks
- PACS and DICOM workflow integration
- Clinical decision support for radiologists
Pricing
- Model
- Freemium
- Category
- Computer Vision
- Rating
- 4.7 / 5 (6)
Use cases
Automated Chest X-Ray Screening
Radiologists use Oxipit to automatically detect abnormalities in chest X-rays, reducing missed findings and supporting more consistent diagnostic interpretation.
Urgent Case Triage
Hospitals prioritize critical chest imaging studies through AI-driven triage, helping radiology teams address time-sensitive cases faster within their PACS workflow.
Radiology Quality Assurance
Imaging departments leverage automated QA checks to catch potential oversights and maintain reporting quality across high-volume chest X-ray workloads.
Reporting Workflow Acceleration
Clinicians streamline radiology report creation using AI-generated findings as decision support, improving documentation efficiency in everyday practice.
Pros & Cons
Pros
- Focused expertise in chest X-ray analysis
- CE-marked for clinical use in Europe
- Integrates with existing PACS and radiology systems
- Supports triage and reporting efficiency
Cons
- Limited primarily to chest imaging modalities
- Requires regulatory approval per region for clinical use
- Adoption depends on hospital IT integration
Reviews
Average from 6 ratings.
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Compared a few options
Evaluated this against two competitors. Where it wins: pACS and DICOM workflow integration and integrates with existing PACS and radiology systems. On balance the feature set — especially quality assurance checks — justifies the 5 stars for our use case.
Solid for our team
We rolled this out across the team last quarter and focused expertise in chest X-ray analysis. Quality assurance checks fits neatly into how we already work, and case prioritization and triage removed a step we used to do by hand. Adoption depends on hospital IT integration, which is the main caveat, but it has held up under daily use.
Solid for our team
We rolled this out across the team last quarter and integrates with existing PACS and radiology systems. Radiology reporting assistance fits neatly into how we already work, and case prioritization and triage removed a step we used to do by hand. but it has held up under daily use.
Years in this space
I've evaluated a lot of these over the years. What stands out here is automated chest X-ray abnormality detection — handled better than most — and cE-marked for clinical use in Europe. Worth the time if this is your use case.
Does the job
Pretty happy overall. Clinical decision support for radiologists just works and integrates with existing PACS and radiology systems. but no dealbreakers — I'd recommend it to a friend without hesitating.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on automated chest X-ray abnormality detection, and focused expertise in chest X-ray analysis caught me off guard. Adoption depends on hospital IT integration is why this isn't a perfect score, still, I'd recommend giving it a real trial.
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