Diagnostic Radiology

Current applications of algorithmic artificial intelligence in interventional radiology: A review of the literature.

Artificial intelligence is a rapidly evolving area of technology whose integration into healthcare delivery infrastructure is predicted to have profound implications for medicine delivery in the 21st century. Artificial intelligence as it relates to healthcare is a term used to cover a wide scope of computer-based algorithms whose application varies from patient selection to enhancements in imagin...

Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence.

Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technol...

Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis.

Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality an...

Legal and ethical principles governing the use of artificial intelligence in radiology services in South Africa.

Artificial intelligence (AI) will drastically change the healthcare system. Radiology is one speciality that is most affected as AI algorithms are inc...

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging.

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. T...

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures.

Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures...

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

BACKGROUND: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially...

Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment.

Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, a...

Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows.

Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for...

Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT and Bard.

Artificial intelligence (AI) has recently become a trending tool and topic regarding productivity especially with publicly available free services suc...

Exploring the performance of implicit neural representations for brain image registration.

Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, w...

Semi-TMS: an efficient regularization-oriented triple-teacher semi-supervised medical image segmentation model.

. Although convolutional neural networks (CNN) and Transformers have performed well in many medical image segmentation tasks, they rely on large amoun...

Artificial Intelligence to Improve Patient Understanding of Radiology Reports.

Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon a...

Understanding calibration of deep neural networks for medical image classification.

Background and Objective - In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also...

Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023).

Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical prediction...

Generative AI in Medical Imaging: Applications, Challenges, and Ethics.

Medical imaging is playing an important role in diagnosis and treatment of diseases. Generative artificial intelligence (AI) have shown great potentia...

New trend in artificial intelligence-based assistive technology for thoracic imaging.

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Dee...

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly a...

"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation.

Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with di...

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup.

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the radiology community about the health equity issue of...