Results 291 to 300 of about 167,942 (343)

Re: “Appropriate Responsibilities and Actions of Radiologists in Radiologic Accidents and Crises”

Journal of the American College of Radiology, 2013
m m m t ( t a t P m u BACKGROUND The 9/11 World Trade Center terrorist attack increased our sensitivity to potential future terrorist attacks. There are more than 100,000 abandoned radioactive sources in the world, approximately 1,000 of which are strong enough to cause harm [1].
Marcus M, Kessler, Mickaila, Johnston, James E, McDonald   +2 more
openaire   +2 more sources

The Growing Nationwide Radiologist Shortage: Current Opportunities and Ongoing Challenges for International Medical Graduate Radiologists.

Radiology
The United States is facing shortages in a myriad of medical fields, including diagnostic radiology (DR). The increasing number of imaging studies, owing to advancing technology and an aging population, is outgrowing the capacity of radiologists. However,
Sohrab Afshari Mirak, S. Tirumani, Nikhil Ramaiya, Inas Mohamed   +3 more
semanticscholar   +1 more source

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Radiology
Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports.
R. Gertz, T. Dratsch, A. Bunck, S. Lennartz, Andra-Iza Iuga, M. Hellmich, T. Persigehl, L. Pennig, C. Gietzen, P. Fervers, D. Maintz, Robert Hahnfeldt, J. Kottlors   +12 more
semanticscholar   +1 more source

RADIOLOGY AND THE RADIOLOGIST

BMJ, 1931
  +5 more sources

Collaboration between clinicians and vision–language models in radiology report generation

Nature Medicine
Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial ...
Ryutaro Tanno, David G. T. Barrett, Andrew Sellergren, Sumedh Ghaisas, Sumanth Dathathri, Abigail See, Johannes Welbl, Charles Lau, Tao Tu, Shekoofeh Azizi, Karan Singhal, Mike Schaekermann, Rhys May, Roy Lee, SiWai Man, S. Mahdavi, Zahra Ahmed, Yossi Matias, Joelle K. Barral, S. M. A. Eslami, Danielle Belgrave, Yun Liu, Sreenivasa Raju Kalidindi, S. Shetty, Vivek Natarajan, Pushmeet Kohli, Po-Sen Huang, A. Karthikesalingam, Ira Ktena   +28 more
semanticscholar   +1 more source

Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases.

Radiology
Background The diagnostic abilities of multimodal large language models (LLMs) using direct image inputs and the impact of the temperature parameter of LLMs remain unexplored.
Pae Sun Suh, Woo Hyun Shim, C. H. Suh, H. Heo, Chae Ri Park, H. Eom, K. Park, J. Choe, Pyeong Hwa Kim, Hyo Jung Park, Yura Ahn, Ho Young Park, Yoonseok Choi, Chang-Yun Woo, H. Park   +14 more
semanticscholar   +1 more source

CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging

International Conference on Medical Image Computing and Computer-Assisted Intervention
Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged as a critical need to alleviate the workload of radiologists. While machine learning has facilitated report
I. Hamamci, Sezgin Er, Bjoern H Menze
semanticscholar   +1 more source

Large Language Models: A Guide for Radiologists

Korean Journal of Radiology
Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to ...
Sunkyu Kim, Choong-kun Lee, Seung-seob Kim   +2 more
semanticscholar   +1 more source

Radiology 2012: radiology and radiologists a decade hence--a strategic analysis for radiology from the second annual American College of Radiology FORUM.

Radiology, 2003
B. Hillman, H. Neiman
semanticscholar   +1 more source

Foundation Models in Radiology: What, How, When, Why and Why Not

Radiology
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data.
Magdalini Paschali, Zhihong Chen, L. Blankemeier, Maya Varma, Alaa Youssef, Christian Bluethgen, Curtis P. Langlotz, S. Gatidis, Akshay S. Chaudhari   +8 more
semanticscholar   +1 more source