Early, accurate and rapid diagnosis of lung cancer is associated with better patient outcomes. Chest X-rays are a high volume test, performed for a spectrum of reasons; lung cancer may either be suspected or an incidental diagnosis. Artificial Intelligence (AI) may help improve the accuracy of lung cancer diagnosis on chest X-ray however, there is little research to enable AI to be embedded in the clinical setting. Due to workforce shortages and rise in diagnostics demand, Radiographers are being increasingly utilised in the reporting of chest x-rays. However, there is limited research studying AI’s impact on the Radiographers’ ability to detect lung cancer.
Therefore, this multi-case, multi reader study, uses an online imaging database and educational platform (RAIQC) and AI software (Qure.ai) with the aim to assess the effectiveness of AI versus simulation education on Radiographer’s accuracy in the detection of lung cancer on chest x-rays.
This package includes image evaluation questions exclusively for participants in the study.
This package includes the following modules:
LungAIM Phase 1
This module includes a collection of chest X-ray cases asking you to comment on the presence or absence of lung cancer.
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- LungAIM Study
Created 21 April, 2023
Last updated 8 August, 2023
Module types included:
- Reader study 1
- Training and assessment cases 53