We are grateful for Artsi et al’s thoughtful letter highlighting crucial challenges and considerations in using large language models (LLMs) to simplify radiology reports and enhance patient understanding.
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Desperate Times Call for Innovative Solutions
Our national radiologist shortage in the United States is a health care crisis that affects all radiology groups, from private practice to academia, and from urban to rural settings. Imaging demands have skyrocketed due to our aging population with increased medical complexity. However, the number of radiology residency training positions has not kept up with the growing demand. As of March 2024, the ACR Career Center lists more than 1,700 radiology jobs for 1,150 radiology residency graduates […
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Bridging the Gap: Evaluation of a Supplemental Surge Staffing Model to Maintain Radiology Turnaround Times Amid Labor Constraints
Many practices face staffing challenges stemming from expanding clinical volumes and recruitment difficulties. This study aims to evaluate the use of supplemental surge staffing along with scheduled shift redistribution as a bridge to needed staffing increases.
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Challenges in Using Large Language Models for Simplifying Radiology Reports
Making medical information clear to patients remains a significant challenge. Innovations in natural language processing (NLP) have improved radiology data interpretation [1]. The rapid adoption of Chat Generative Pre-trained Transformer (ChatGPT) highlights a pivotal moment in NLP. This “ChatGPT moment” suggests a promising application in bridging the gap between patients and their medical data.
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Excess Greenhouse Gas Emissions Associated With Inappropriate Medical Imaging in the US Medicare Part B Population From 2017 to 2021
Medical imaging is a source of greenhouse gas emissions, and inappropriate use results in low-value, excess imaging. The environmental impact of low-value imaging has not been quantified.