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Knowledge domain and frontier trends of artificial intelligence applied in solid organ transplantation: A visualization analysis.
Int J Med Inform
December 2024
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. Electronic address:
Background: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored.
View Article and Find Full Text PDFAutonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFIn this article, the authors propose a repurposing of the concept of entrustment to help guide the use of artificial intelligence (AI) in health professions education (HPE). Entrustment can help identify and mitigate the risks of incorporating generative AI tools with limited transparency about their accuracy, source material, and disclosure of bias into HPE practice. With AI's growing role in education-related activities, like automated medical school application screening and feedback quality and content appraisal, there is a critical need for a trust-based approach to ensure these technologies are beneficial and safe.
View Article and Find Full Text PDFValidating a Practical Correction for Intravenous Contrast on Computed Tomography-Based Muscle Density.
J Comput Assist Tomogr
November 2024
From the Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI.
Objective: Computed tomography (CT) measured muscle density is prognostic of health outcomes. However, the use of intravenous contrast obscures prognoses by artificially increasing CT muscle density. We previously established a correction to equalize contrast and noncontrast muscle density measurements.
View Article and Find Full Text PDFDevelopment and Clinical Evaluation of a Contrast Optimizer for Contrast-Enhanced CT Imaging of the Liver.
J Comput Assist Tomogr
November 2024
From the Carl E. Ravin Advanced Imaging Labs, Center for Virtual Imaging Trials, Department of Radiology.
Objective: Patient characteristics, iodine injection, and scanning parameters can impact the quality and consistency of contrast enhancement of hepatic parenchyma in CT imaging. Improving the consistency and adequacy of contrast enhancement can enhance diagnostic accuracy and reduce clinical practice variability, with added positive implications for safety and cost-effectiveness in the use of contrast medium. We developed a clinical tool that uses patient attributes (height, weight, sex, age) to predict hepatic enhancement and suggest alternative injection/scanning parameters to optimize the procedure.
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