AIMC Topic: Radiologists

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Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports.

European journal of radiology Feb 6, 2025
BACKGROUND: Large language models (LLMs) often struggle to fully capture the nuanced preferences and clinical judgement of radiologists in medical report summarization even when fine-tuned on massive medical reports. This could lead to the generated ...
Reinforcement, Psychology Tomography, X-Ray Computed Female Aged Feedback Male Middle Aged Radiologists Radiography, Thoracic Large Language Models Humans Artificial Intelligence Machine Learning Retrospective Studies
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Large language models in methodological quality evaluation of radiomics research based on METRICS: ChatGPT vs NotebookLM vs radiologist.

European journal of radiology Jan 29, 2025
OBJECTIVES: This study aimed to evaluate the effectiveness of large language models (LLM) in assessing the methodological quality of radiomics research, using METhodological RadiomICs Score (METRICS) tool.
Humans Language Reproducibility of Results Radiology Radiomics Radiologists Biomedical Research Large Language Models Generative Artificial Intelligence Natural Language Processing
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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...
Female Aged Aged, 80 and over Adult Sensitivity and Specificity Humans Artificial Intelligence Radiologists Colorectal Neoplasms Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Male Colonic Polyps Adenomatous Polyps Colonography, Computed Tomographic 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 Rib Fractures Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Female Aged Aged, 80 and over Adult Sensitivity and Specificity Male Middle Aged Humans Artificial Intelligence
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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.
Robotics Radiologists Feasibility Studies Telemedicine Learning Curve Middle Aged China Clinical Competence Ultrasonography Humans Prospective Studies Female Aged Aged, 80 and over Young Adult Adult Male
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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 Diagnostic Errors Eye-Tracking Technology Clinical Competence Eye Movements Humans Machine Learning
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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 ...
Female Aged Adult Diagnostic Errors Male Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Middle Aged Missed Diagnosis Radiologists Cervical Vertebrae Spinal Fractures
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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 Radiologists Radiography, Thoracic Internal Medicine Humans Female Home Care Services Artificial Intelligence Aged Software Aged, 80 and over 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.
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Radiologists Databases, Factual Neural Networks, Computer Image Interpretation, Computer-Assisted Brain Machine Learning Retrospective Studies Algorithms Reproducibility of Results
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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...
Observer Variation Coronary Vessels Multidetector Computed Tomography Vascular Calcification Computed Tomography Angiography Time Factors Radiologists Humans Cardiac-Gated Imaging Techniques Electrocardiography Radiographic Image Interpretation, Computer-Assisted Artificial Intelligence Predictive Value of Tests Coronary Artery Disease Retrospective Studies Reproducibility of Results Coronary Angiography Female Workflow Aged Male Middle Aged Severity of Illness Index
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