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Diagnostic Radiologist - Europe PMC

Summarized by Plex Scholar
Last Updated: 15 May 2022

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A Clinically Optimal Protocol for the Imaging of Enteric Tubes: On the Basis of Radiologist Interpreted Diagnostic Utility and Radiation Dose Reduction.

Developing and evaluating a patient thickness-based protocol specifically for the detection of enteric tube placements in bedside abdominal radiographs was the primary aim of this investigation. Protocol strategies were designed to maintain image quality while simultaneously minimizing patient doses. Seven expert radiologists based on a single conspicuity scale randomized and graded radiographs for diagnostic quality. Basic patient demographics, body mass index, ventilation status, and enteric tube type were gathered, as well as subgroup analyses were conducted. The customised thickness-based protocol resulted in a significant reduction in the effective dose of 80%. In the thickness-based protocol, there was no significant difference in diagnostic quality between the two cohorts with 209 diagnostic radiographs in the baseline and 221 diagnostic radiographs. The development of a protocol for the reporting of enteric tube placements was successful. The protocol design process that was described in this study could be easily adaptable to other imaging clinical tasks.

Source link: https://europepmc.org/article/MED/35504809


Non-radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports.

Incorporating artificial intelligence in diagnostic medical imaging studies has the ability to increase productivity. Although the assumption of radiologists, radiographers, medical students, and patients of AI use in image reporting has been investigated, there is no literature on non-radiologist clinicians' opinion on this topic. Clinicians from all levels and specialties felt significantly less confident working on AI-issued reports than with radiologist-issued ones, but equally comfortable with an AI-hybrid framework of care. Doctors, health service providers, and radiologists accounted for the majority of errors in AI-issued journals, according to non-radiologist clinicians. Non-radiologist clinicians were significantly less concerned with AI issuing image reports rather than radiologists, which was concerning data privacy and security. Based on the opinions of our referring non-radiologist medical colleagues, a hybrid AI-generated image reporting system may be the most effective way to integrate AI into clinical practice.

Source link: https://europepmc.org/article/MED/35191186

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions