Rationale And Objectives: The objective of this study was to evaluate the effectiveness of a pilot artificial intelligence (AI) certificate program in aiding radiology trainees to develop an understanding of the evolving role and application of artificial intelligence in radiology. A secondary objective was set to determine the background of residents that would most benefit from such training.
Materials And Methods: This was a prospective pilot study involving 42 radiology residents at two separate residency programs who participated in the Radiological Society of North America Imaging AI Foundational Certificate course over a four-month period. The course consisted of 6 online modules that contained didactic lectures followed by end-of-module quizzes to assess knowledge gained from these lectures. Pre- and post-course assessments were conducted to evaluate the residents' knowledge and skills in AI. Additionally, a post-course survey was performed to assess participants' overall satisfaction with the course.
Results: All participating residents completed the certificate program. The mean pre-course assessment score was 37 %, which increased to 73 % after completing the modules (p < 0.001). 74 % (31/42) endorsed the belief the course improved familiarity with artificial intelligence in radiology. Residency program, residency year, and reported prior familiarity with AI were not found to influence pre-course score, post-course score, nor score improvement. 57 % (24/42) endorsed interest in pursuing further certification in AI.
Conclusion: Our pilot study suggests that a certificate course can effectively enhance the knowledge and skills of radiology residents in the application of AI in radiology. The benefits of such a course can be found regardless of program, resident year, and self-reported prior resident understanding of radiology in AI.
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http://dx.doi.org/10.1016/j.acra.2024.05.041 | DOI Listing |
J Health Organ Manag
January 2025
University of Malta, Msida, Malta.
Purpose: This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase. The aim is to showcase how this fusion can help tackle healthcare inequalities, enhance accessibility and support long-term sustainability.
Design/methodology/approach: Adopting a viewpoint approach, the study leverages existing literature and case studies to analyze the intersection of CSR and AI.
BMC Med Inform Decis Mak
January 2025
QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.
View Article and Find Full Text PDFBMC Emerg Med
January 2025
Emergency department, CHR Metz-Thionville, Metz, 57000, France.
Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
J Gen Intern Med
January 2025
VA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.
Background: Advances in artificial intelligence and machine learning have facilitated the creation of mortality prediction models which are increasingly used to assess quality of care and inform clinical practice. One open question is whether a hospital should utilize a mortality model trained from a diverse nationwide dataset or use a model developed primarily from their local hospital data.
Objective: To compare performance of a single-hospital, 30-day all-cause mortality model against an established national benchmark on the task of mortality prediction.
Eur J Nucl Med Mol Imaging
January 2025
Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
This joint practice guideline/procedure standard was collaboratively developed by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neuro-Oncology (EANO), and the PET task force of the Response Assessment in Neurooncology Working Group (PET/RANO). Brain metastases are the most common malignant central nervous system (CNS) tumors. PET imaging with radiolabeled amino acids and to lesser extent [F]FDG has gained considerable importance in the assessment of brain metastases, especially for the differential diagnosis between recurrent metastases and treatment-related changes which remains a limitation using conventional MRI.
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