AIMC Topic: Radiology
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Appropriate Reliance on Artificial Intelligence in Radiology Education.
Journal of the American College of Radiology : JACR
Jun 29, 2023
Users of artificial intelligence (AI) can become overreliant on AI, negatively affecting the performance of human-AI teams. For a future in which radiologists use interpretive AI tools routinely in clinical practice, radiology education will need to ...
Commercially Available Artificial Intelligence Algorithms of Interest to Pediatric Radiology: The Growing Gap Between Potential Use and Data Training.
Journal of the American College of Radiology : JACR
Jun 29, 2023
Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists.
Academic radiology
Jun 21, 2023
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) is changing radiology by automating tasks and assisting in abnormality detection and understanding perceptions of medical students, radiology trainees, and radiologists is vital for preparing the...
Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development.
Journal of the American College of Radiology : JACR
Jun 21, 2023
Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum f...
How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders.
European journal of radiology
Jun 8, 2023
PURPOSE: In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey.
AI vs FRCR: What it means for the future.
European journal of radiology
Jun 8, 2023
A recent work by Shelmerdine et al. was published in the Christmas edition of the BMJ. The authors were inspired by George Hinton's statement that artificial intelligence (AI) would supersede radiologists, and ventured to investigate whether the AI s...
Using deep learning-derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data.
European radiology
Jun 7, 2023
OBJECTIVES: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS.
Enhancement of Non-Linear Deep Learning Model by Adjusting Confounding Variables for Bone Age Estimation in Pediatric Hand X-rays.
Journal of digital imaging
Jun 2, 2023
In medicine, confounding variables in a generalized linear model are often adjusted; however, these variables have not yet been exploited in a non-linear deep learning model. Sex plays important role in bone age estimation, and non-linear deep learni...
Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing.
La Radiologia medica
May 29, 2023
Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiol...
The regulatory environment for artificial intelligence-enabled devices in the United States.
Seminars in vascular surgery
May 27, 2023
The regulatory environment in the United States has not kept pace with the rapidly developing market for artificial intelligence (AI)-enabled devices. The number of AI-enabled devices has increased year after year. All of these devices are registered...
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