AIMC Topic: Radiologists

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Effect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.

European radiology Jan 25, 2025
OBJECTIVES: Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and n...
Aged Aged, 80 and over Adult Sensitivity and Specificity Humans Artificial Intelligence Retrospective Studies Female Radiologists Radiographic Image Interpretation, Computer-Assisted Colonic Polyps Adenomatous Polyps Colonography, Computed Tomographic Colorectal Neoplasms Male Middle Aged Diagnosis, Differential
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AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.

PloS one Jan 24, 2025
To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma pat...
Radiologists Radiographic Image Interpretation, Computer-Assisted Adult Sensitivity and Specificity Male Middle Aged Tomography, X-Ray Computed Female Aged Aged, 80 and over Humans Artificial Intelligence Rib Fractures
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Diagnostic Performance of Different Examination Types and Learning Curves of Radiologists for 5G-Based Robot-Assisted Tele-Ultrasonography: A Prospective and Large-Scale Study.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine Jan 22, 2025
OBJECTIVE: To investigate the feasibility of remotely providing routine ultrasound (US) examinations to patients using a fifth-generation-based robot-assisted tele-ultrasonography (RATU) system in a real-world setting.
Aged Aged, 80 and over Young Adult Adult Humans Prospective Studies Female Ultrasonography Robotics Radiologists Feasibility Studies Male Middle Aged China Clinical Competence Telemedicine Learning Curve
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Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

JMIR formative research Jan 22, 2025
BACKGROUND: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions ...
Radiologists Humans Machine Learning Diagnostic Errors Eye-Tracking Technology Clinical Competence Eye Movements
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Machine Learning to Detect Cervical Spine Fractures Missed by Radiologists on CT: Analysis Using Seven Award-Winning Models From the 2022 RSNA Cervical Spine Fracture AI Challenge.

AJR. American journal of roentgenology Jan 8, 2025
Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on reevaluation; such studies do not capture the full extent of missed fractures. The purpose of this study was ...
Adult Diagnostic Errors Male Retrospective Studies Middle Aged Tomography, X-Ray Computed Radiologists Female Humans Cervical Vertebrae Aged Machine Learning Spinal Fractures Missed Diagnosis
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Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study.

JMIR formative research Dec 24, 2024
BACKGROUND: Home hospitalization is a care modality growing in popularity worldwide. Telemedicine-driven hospital-at-home (HAH) services could replace traditional hospital departments for selected patients. Chest x-rays typically serve as a key diagn...
Consensus Home Care Services Humans Female Radiologists Artificial Intelligence Aged Radiography, Thoracic Software Aged, 80 and over Internal Medicine Prospective Studies Male
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Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.

Journal of magnetic resonance imaging : JMRI Dec 17, 2024
BACKGROUND: Deep learning (DL) often requires an image quality metric; however, widely used metrics are not designed for medical images.
Reproducibility of Results Humans Neural Networks, Computer Deep Learning Machine Learning Image Processing, Computer-Assisted Algorithms Radiologists Magnetic Resonance Imaging Databases, Factual Image Interpretation, Computer-Assisted Brain Retrospective Studies
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Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

The international journal of cardiovascular imaging Dec 16, 2024
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients...
Severity of Illness Index Time Factors Electrocardiography Predictive Value of Tests Coronary Angiography Radiologists Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Reproducibility of Results Aged Male Middle Aged Humans Vascular Calcification Artificial Intelligence Computed Tomography Angiography Workflow Retrospective Studies Cardiac-Gated Imaging Techniques Observer Variation Female Coronary Vessels Multidetector Computed Tomography
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ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists' intentions.

Artificial intelligence in medicine Dec 12, 2024
Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical domain. However, a notable challenge is the lack of explainability of many advanced models, which p...
Diagnosis, Computer-Assisted Deep Learning Humans Radiologists Artificial Intelligence Intention
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Release of complex imaging reports to patients, do radiologists trust AI to help?

Current problems in diagnostic radiology Dec 10, 2024
BACKGROUND: As a result of the 21st Century Cures Act, radiology reports are immediately released to patients. However, these reports are often too complex for the lay patient, potentially leading to stress and anxiety. While solutions such as patien...
Artificial Intelligence Trust Humans Surveys and Questionnaires Diagnostic Imaging Attitude of Health Personnel Radiologists
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