AIMC Topic: Radiology

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Causal insights from clinical information in radiology: Enhancing future multimodal AI development.

Computer methods and programs in biomedicine
PURPOSE: This study investigates the causal mechanisms underlying radiology report generation by analyzing how clinical information and prior imaging examinations contribute to annotation shifts. We systematically estimate why and how biases manifest...

Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality.

Cancer research
The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques pro...

Ultimate focus: applications of the Churchill Method in radiology.

Clinical imaging
The Churchill Method evolved as an approach to shooting sporting clays; essentially, successfully shooting the clay as it followed its multi-dimensional trajectory could be distilled into a simplified task, with well-trained instinct taking over to a...

A comparison of performance of DeepSeek-R1 model-generated responses to musculoskeletal radiology queries against ChatGPT-4 and ChatGPT-4o - A feasibility study.

Clinical imaging
OBJECTIVE: Artificial Intelligence (AI) has transformed society and chatbots using Large Language Models (LLM) are playing an increasing role in scientific research. This study aims to assess and compare the efficacy of newer DeepSeek R1 and ChatGPT-...

The Evolution of Radiology Image Annotation in the Era of Large Language Models.

Radiology. Artificial intelligence
Although there are relatively few diverse, high-quality medical imaging datasets on which to train computer vision artificial intelligence models, even fewer datasets contain expertly classified observations that can be repurposed to train or test su...

The radiologist and data: Do we add value or is data just data?

Clinical imaging
Artificial intelligence in radiology critically depends on vast amounts of quality data, and there are controversies surrounding the topic of data ownership. In the current clinical framework, the secondary use of clinical data should be treated as a...

Expanded AI learning: AI as a Tool for Human Learning.

Academic radiology
RATIONALE AND OBJECTIVES: To demonstrate that a deep learning (DL) model can be employed as a teaching tool to improve radiologists' ability to perform a subsequent imaging task without additional artificial intelligence (AI) assistance at time of im...

Evaluating artificial intelligence chatbots for patient education in oral and maxillofacial radiology.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to compare the quality and readability of the responses generated by 3 publicly available artificial intelligence (AI) chatbots in answering frequently asked questions (FAQs) related to Oral and Maxillofacial Radiology (OM...

Pitfalls and Best Practices in Evaluation of AI Algorithmic Biases in Radiology.

Radiology
Despite growing awareness of problems with fairness in artificial intelligence (AI) models in radiology, evaluation of algorithmic biases, or AI biases, remains challenging due to various complexities. These include incomplete reporting of demographi...

Artificial intelligence software in biomedical imaging: a pharmaceutical perspective on radiology and contrast-enhanced ultrasound applications.

Clinical and experimental rheumatology
Artificial intelligence (AI) is rapidly transforming radiology, with over 200 CE-marked products in the EU and more than 750 AI-based devices authorised by the FDA in the US, mainly used for x-ray, CT, MRI, and ultrasound imaging. Despite regulatory ...