An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence.

Introduction: Artificial intelligence (AI) is being used increasingly in image interpretation tasks. Human reliance on technology and bias can cause decision errors. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene.

Accuracy of interpretation of nasogastric tube position on chest radiographs by diagnostic radiographers: A multi-case, multi-reader study.

Introduction: Feeding via a misplaced nasogastric tube (NGT) is a common but preventable cause of patient harm. The aim of this study was to determine the accuracy of diagnostic radiographers' assessment of NGT position on chest radiographs (CXRs) and safe-to-feed decisions.

Methods: A multi-case, multi-reader study was conducted using an online image interpretation platform.

Artificial intelligence and radiographer preliminary image evaluation: What might the future hold for radiographers providing x-ray interpretation in the acute setting?

In a stretched healthcare system, radiographer preliminary image evaluation in the acute setting can be a means to optimise patient care by reducing error and increasing efficiencies in the patient journey. Radiographers have shown impressive accuracies in the provision of these initial evaluations, however, barriers such as a lack of confidence and increased workloads have been cited as a reason for radiographer reticence in engagement with this practice. With advances in Artificial Intelligence (AI) technology for assistance in clinical decision-making, and indication that this may increase confidence in diagnostic decision-making with reporting radiographers, the author of this editorial wonders what the impact of this technology might be on clinical decision-making by radiographers in the provision of Preliminary Image Evaluation (PIE).

Reporting radiographers' interaction with Artificial Intelligence-How do different forms of AI feedback impact trust and decision switching?

Artificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services.