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

Šeškinės Poliklinika deployed Computer-Aided Diagnosis for Medical Imaging & Radiology in Ambulatory & Outpatient. As reported by oxipit.ai: 80% autonomous reporting rate (occupational cxrs).

Maintained by Peter Korpak, Lead EditorHow evidence is checked
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)

What Šeškinės Poliklinika was trying to fix

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.

What Šeškinės Poliklinika deployed

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.

Evidence for Šeškinės Poliklinika's Medical Imaging & Radiology deployment

Reported outcome metrics
3 cited below
Cited source
oxipit.ai
Last updated
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