AIMC Topic: Radiology

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Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in...

A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

European journal of radiology
OBJECTIVES: This study aims to contribute to an understanding of the explainability of computer aided diagnosis studies in radiology that use end-to-end deep learning by providing a quantitative overview of methodological choices and by discussing th...

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.

Clinical chemistry and laboratory medicine
OBJECTIVES: The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, ...

Introduction to the Veterinary Radiology & Ultrasound Special Issue on Artificial Intelligence.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association

A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

Annals of emergency medicine
STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represent unrecognized serious medical conditions, including cancer. Recognition of incidental findings freq...

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.

PloS one
Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image data...

Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the effectiveness of an artificial intelligence (AI) in radiology literacy course on participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States.

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables.

Artificial intelligence in medicine
Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a de...

The effect of an artificial intelligence algorithm on chest X-ray interpretation of radiology residents.

The British journal of radiology
OBJECTIVE: Chest X-rays are the most commonly performed diagnostic examinations. An artificial intelligence (AI) system that evaluates the images fast and accurately help reducing workflow and management of the patients. An automated assistant may re...

Artificial Intelligence in Lung Imaging.

Seminars in respiratory and critical care medicine
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging...