AIMC Topic: Radiology

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A deep learning approach for projection and body-side classification in musculoskeletal radiographs.

European radiology experimental
BACKGROUND: The growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management and automated metadata classification systems. We developed a large-scale, well-characterized datase...

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI.

Journal of the American College of Radiology : JACR
Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 20...

Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment.

Clinical radiology
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors s...

Empowering PET: harnessing deep learning for improved clinical insight.

European radiology experimental
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough explor...

Performance of AI chatbots on controversial topics in oral medicine, pathology, and radiology.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVES: In this study, we assessed 6 different artificial intelligence (AI) chatbots (Bing, GPT-3.5, GPT-4, Google Bard, Claude, Sage) responses to controversial and difficult questions in oral pathology, oral medicine, and oral radiology.

Image annotation and curation in radiology: an overview for machine learning practitioners.

European radiology experimental
"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and d...

Generative Artificial Intelligence.

Journal of the American College of Radiology : JACR

Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning.

European journal of radiology
X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance...

[Not Available].

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin

Bidirectional Encoder Representations from Transformers in Radiology: A Systematic Review of Natural Language Processing Applications.

Journal of the American College of Radiology : JACR
INTRODUCTION: Bidirectional Encoder Representations from Transformers (BERT), introduced in 2018, has revolutionized natural language processing. Its bidirectional understanding of word context has enabled innovative applications, notably in radiolog...