Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.

Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiographs, where AI can correctly exclude pathology (ie, specificity) without increasing diagnostic errors. Materials and Methods In this retrospective study, consecutive chest radiographs in unique adult patients (≥18 years of age) were obtained January 1-12, 2020, at four Danish hospitals.

Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion.

Background Commercially available artificial intelligence (AI) tools can assist radiologists in interpreting chest radiographs, but their real-life diagnostic accuracy remains unclear. Purpose To evaluate the diagnostic accuracy of four commercially available AI tools for detection of airspace disease, pneumothorax, and pleural effusion on chest radiographs. Materials and Methods This retrospective study included consecutive adult patients who underwent chest radiography at one of four Danish hospitals in January 2020.

Autonomous Chest Radiograph Reporting Using AI: Estimation of Clinical Impact.

Background Automated interpretation of normal chest radiographs could alleviate the workload of radiologists. However, the performance of such an artificial intelligence (AI) tool compared with clinical radiology reports has not been established. Purpose To perform an external evaluation of a commercially available AI tool for the number of chest radiographs autonomously reported, the sensitivity for AI detection of abnormal chest radiographs, and the performance of AI compared with that of the clinical radiology reports.

Many unexpected abdominal findings on staging computed tomography in patients with colorectal cancer.

Introduction: Computed tomography (CT) was proven to be superior to preoperative abdominal ultrasound in the preoperative setting for detection of hepatic metastases from colorectal cancer (CRC). The higher sensitivity of CT has resulted in a number of unexpected abdominal findings of varying importance; an issue that was previously studied in relation to CT colonography, but not in relation to staging CT with intravenous contrast in CRC patients. The aim of the present study was to evaluate the number and significance of such unexpected findings on staging CTs in CRC patients.