Oxipit.ai

AI-powered computer vision for medical imaging workflows.

4.7 (6)

리뷰어 Daniel Nikulshyn·업데이트됨 2026년 5월

Enterprise PACS Integration Radiology Computer Vision Clinical AI Medical Imaging Healthcare

개요

Oxipit develops AI software that assists radiologists in interpreting medical images, with a focus on chest X-rays. Its tools help clinicians identify abnormalities, prioritize urgent cases, and streamline reporting in everyday diagnostic practice. The platform is designed to integrate with hospital PACS and radiology workflows, providing automated findings, quality control checks, and case triage. Oxipit's solutions have received regulatory clearance in multiple regions, including CE marking in Europe.

주요 기능

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

사용 사례

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.

장단점

장점

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

단점

Limited primarily to chest imaging modalities
Requires regulatory approval per region for clinical use
Adoption depends on hospital IT integration

리뷰

4.7

6개 평가의 평균.

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Grace Okafor

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.

Jamal Carter

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.

Hiroshi Tanaka

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.

Mei-Ling Wong

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.

Diego Fernández

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.

Olga Ivanova

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.