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The use of large language models in detecting Chinese ultrasound report errors.
NPJ Digit Med
January 2025
Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
This retrospective study evaluated the efficacy of large language models (LLMs) in improving the accuracy of Chinese ultrasound reports. Data from three hospitals (January-April 2024) including 400 reports with 243 errors across six categories were analyzed. Three GPT versions and Claude 3.
View Article and Find Full Text PDFA phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.
NPJ Digit Med
January 2025
Department of Computer Science and Technology & Institute for Artificial Intelligence & BNRist, Tsinghua University, Beijing, China.
Rare diseases, affecting ~350 million people worldwide, pose significant challenges in clinical diagnosis due to the lack of experienced physicians and the complexity of differentiating between numerous rare diseases. To address these challenges, we introduce PhenoBrain, a fully automated artificial intelligence pipeline. PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases.
View Article and Find Full Text PDFLarge language models improve the identification of emergency department visits for symptomatic kidney stones.
Sci Rep
January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
View Article and Find Full Text PDFStroke Triage Alpha Rescue Score (STARS): Prehospital phone-based tool to identify cerebral large vessel occlusion.
Rev Neurol (Paris)
January 2025
Unité neurovasculaire, Centre Hospitalier Métropole Savoie, Chambéry, France. Electronic address:
Introduction: Prehospital identification of stroke patients with large vessel occlusion (LVO) is crucial to optimize transport to an endovascular thrombectomy (EVT)-capable center. Existing scores require medical or paramedical expertise and specific teachings. We aimed to validate a simple prehospital phone-based score for LVO identification.
View Article and Find Full Text PDFDo people prefer AI-generated patient educational materials over traditional ones?
Patient Educ Couns
January 2025
University of Texas at Austin Dell Medical School, Austin, TX, United States.
Objective: This study aimed to assess people's preference between traditional and Artificial Intelligence (AI)-generated colon cancer staging Patient Education Materials (PEMs).
Methods: We assessed preference among patients and companions being seen for a non-cancer diagnosis at the UT Health Austin Colon and Rectal Surgery Clinic. Participants were blinded to the study concept of AI and generation method of PEMs (Traditional: National Cancer Institute and the American Cancer Society; AI-generated: ChatGPT and Google Bard).
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