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Šeškinės Poliklinika autonomously reports 80% of occupational health chest X-rays with Oxipit CXR Suite

“Šeškinės Poliklinika autonomously reports 80% of occupational health chest X-rays with Oxipit CXR Suite” documents a Medical Imaging & Radiology deployment in Ambulatory & Outpatient at Šeškinės Poliklinika. oxipit.ai reports autonomous reporting rate (occupational cxrs): 80%; this directory has not independently verified that result.

Maintained by Peter Korpak, Founder & Chief AnalystHow evidence is checked

Evidence at a glance

Evidence status:
Automated evidence gate passed
Deployment timeframe:
Not reported by source
Reported outcome metrics:
3 cited below
Directory entry published:
Source link checked:

The source-link check confirms reachability, not independent re-verification of every claim.

80%Autonomous Reporting Rate (Occupational CXRs)
2,700+Monthly Chest X-Rays Processed
~1 per 30–60 daysQA Discrepancy Alert Frequency

Source-reported figures — cited source: oxipit.ai

The Challenge

Šeškinės Poliklinika processes over 2,700 chest X-rays monthly and faced increasing diagnostic demand with limited time and resources. Radiologists needed support to maintain consistent diagnostic quality across a high volume of routine healthy-patient scans, while ensuring subtle findings were not missed.

The Solution

The clinic implemented Oxipit CXR Suite over four years, integrating two AI tools directly into the PACS: a real-time quality assurance module that flags discrepancies between AI findings and radiologist reports, and ChestLink, an autonomous reporting tool that automatically clears normal occupational health chest X-rays without requiring radiologist review.

Results

AI now autonomously reports approximately 80% of occupational (prophylactic) chest X-rays, eliminating the routine review bottleneck and allowing examining physicians to proceed immediately. Referring physicians gain real-time AI impressions in PACS, accelerating triage during high-volume periods like flu season. The QA system provides a safety net with rare but clinically valuable discrepancy alerts.

Key Takeaways

  • Seamless PACS integration was the critical adoption driver — resistance faded once AI became part of the native workflow rather than a separate step.
  • A collaborative, non-punitive approach to AI-flagged discrepancies (reviewing together rather than assigning blame) sustains radiologist trust and long-term use.
  • Occupational health scans (high-volume, low-risk, well-defined normal criteria) are an effective entry point for autonomous AI reporting before expanding to more complex modalities.

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Details

Company Size
MidMarket
Evidence status
Automated evidence gate passed
Deployment timeframe
Not reported by source
Directory entry published
Source link checked

Cited source

oxipit.ai

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