الفهرس 
Patient preparationOnly 14 pages are availabe for public view
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Abstract
Background: Artificial intelligence (AI) is a branch of computer science in which statistical techniques are used to classify, simulate, or optimize data based on previous observations. An AI system can interpret and prioritize abnormal chest X-rays with critical findings, potentially reducing the backlog of exams and bringing urgently needed care to patients more quickly.
Aim of the Work: Our study aims to test the diagnostic performance of the AI system in PICU CXR interpretation regarding its sensitivity, specificity, as well as its accuracy. we also investigate the effect of AI aid on residents performance regarding diagnostic accuracy, sensitivity time consumption.
Patients and Methods: This was a Cross sectional study which conducted at Pediatric Ain Shams University Hospitals from January to June 2021. The study was performed on convenient sample consists of 30 CXRs done at the Ain Shams Pediatric hospital intensive care unit and meeting the study inclusion criteria.
Results: Overall in this study we found that the resident diagnostic accuracy, sensitivity and time consumption improved across a large number of clinical CXRs findings as well as strengthening of the residents agreement with the expert radiologist when assisted by the deep-learning model. Effective implementation of the model has the potential to improve clinical practice. Research is underway to assess the generalizability of results to various clinical environments and health systems.
Conclusion: We raised the possibility of the potential use of AI systems in future radiology workflows for preliminary interpretations that target the most prevalent findings, leaving the final reads to be done by a more experienced radiologist in a suitable practice atmosphere to catch any potential misses from the less-prevalent fine-grained findings.