5C Network's chest X-ray AI pre-reads every study in 10–20 seconds and flags findings for a radiologist, who confirms them and signs the report. In a study across 17 healthcare systems it reached 98% precision and 95% recall on the 74 pathologies evaluated.
Quick answer
Study arrives from any PACS. Bionic Vision processes standard DICOM formats from any modality manufacturer.
Deep learning models trained on 4B+ clinical images analyze the study in 10–20 seconds, flagging all detected abnormalities with confidence scores.
Specialist radiologists review AI findings, confirm or modify detections, and sign the final report. Human-in-the-loop ensures clinical accuracy.
Final report delivered in 30 minutes average turnaround (vs 24–48 hour industry standard). Critical findings escalated immediately.
The multi-site study evaluated 74 chest X-ray pathologies in eight categories. Examples from the evaluated set:
The methodology and validation behind 5C Network's chest X-ray AI are published as preprints on arXiv. Full results are on the evidence page.
Multi-site study evaluating autonomous AI for detecting multiple pathologies in chest X-rays across the Indian healthcare system.
Multimodal approach combining chest X-ray imaging with clinical data for enhanced TB detection.
Precision analysis framework for chronic TB cases using advanced vision-language techniques.
AI flags critical findings (pneumothorax, large effusions) in seconds, enabling rapid prioritization of emergency cases.
High-volume TB screening with vision-language models that combine imaging with clinical data for improved detection.
Daily portable X-rays analyzed for tube malposition, new infiltrates, and developing complications before clinical signs appear.
AI-native teleradiology brings specialist-level reads to facilities without on-site radiologists. 30-minute turnaround regardless of location.
Chest X-ray AI is software that reads a chest radiograph, flags abnormal findings and pre-fills a structured report for a radiologist to confirm. 5C Network's chest X-ray AI, part of Bionic AI, pre-reads the study in 10–20 seconds; a radiologist reviews every finding and signs the report.
5C Network's chest X-ray AI reached 98% precision and 95% recall across the 74 pathologies evaluated in a multi-site study across 17 healthcare systems, with a mean AUC of 0.97 and no evaluated pathology below 0.95. Those figures describe chest X-ray on the evaluated set, not every modality.
5C Network's study evaluated 74 chest X-ray pathologies in eight categories: foreign bodies and devices, fractures and musculoskeletal findings, pleural conditions, lung parenchymal abnormalities, infections including tuberculosis, cardiac and mediastinal findings, collapse and volume loss, and other findings. Per-pathology AUC is on the evidence page.
No. At 5C Network the AI pre-reads and flags; a radiologist confirms or edits each finding and signs the report. In India the signed diagnostic report must come from a registered medical practitioner, so AI assists and the radiologist diagnoses.
Yes. Tuberculosis is one of the pathologies 5C Network's chest X-ray AI was evaluated on, and 5C Network has published two preprints on vision-language models for acute and chronic TB diagnosis that combine the X-ray with clinical data.
Join 2,000+ hospitals using 5C Network for AI-powered radiology. 30-minute turnaround, 74 chest X-ray pathologies evaluated, a radiologist signing every report.
