Radiology

Diagnostic Radiology

Latest AI and machine learning research in diagnostic radiology for healthcare professionals.

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Showing 22-42 of 2,760 articles
Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.

BACKGROUND: Artificial intelligence (AI) is rapidly advancing in health care, particularly in medica...

Documenting Disclosure: Limited Reporting of Generative AI Usage in Radiology Research Manuscripts.

RATIONALE AND OBJECTIVES: Large language models (LLMs) show promise in radiology through various cli...

Objective assessment of diagnostic image quality in CT scans: what radiologists and researchers need to know.

OBJECTIVES: Quantifying diagnostic image quality (IQ) is not straightforward but essential for optim...

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiolog...

Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study.

Education for medical imaging technologists or radiographers in regional and rural areas often faces...

Radiology report generation using automatic keyword adaptation, frequency-based multi-label classification and text-to-text large language models.

BACKGROUND: Radiology reports are essential in medical imaging, providing critical insights for diag...

Diagnostic Performance of ChatGPT-4o and DeepSeek-3 Differential Diagnosis of Complex Oral Lesions: A Multimodal Imaging and Case Difficulty Analysis.

BACKGROUND: AI models like ChatGPT-4o and DeepSeek-3 show diagnostic promise, but their reliability ...

Artificial intelligence in cardiovascular imaging: risks, mitigations and the path to safe implementation.

Artificial intelligence (AI) is rapidly transforming cardiovascular imaging by automating tasks such...

Practical applications of AI in body imaging.

Artificial intelligence (AI) algorithms and deep learning continue to change the landscape of radiol...

Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.

PURPOSE: Radiology reports are essential for communicating imaging findings to guide diagnosis and t...

Diagnostic Study of Head and Neck Metastatic Tumors From Different Primary Sites Based on Stacking Machine Learning Methods.

Metastatic tumors of the head and neck (MTHN) typically indicate advanced disease with a poor progno...

Fourier ptychography microscopy for digital pathology.

Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, tha...

Diagnostic Performance of ChatGPT-4o in Detecting Hip Fractures on Pelvic X-rays.

INTRODUCTION: Hip fractures are a major orthopedic problem, especially in the elderly population. Hi...

[Incidental pulmonary nodules on CT imaging: what to do?].

Incidental pulmonary nodules are very frequently found on CT imaging and may represent (early stage)...

Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging.

Magnetic resonance spectroscopic imaging has potential for non-invasive metabolic imaging of the hum...

Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging.

Deep generative models (DGMs) have been studied and developed primarily in the context of natural im...

Emerging Trends in Point-of-Care Technology Development for Oncology in Low- and Middle-Income Countries.

The growing cancer burden and suboptimal diagnostic capacity in low- and middle-income countries cal...

Canadian Radiology Update.

Radiology research at Canadian institutions is advancing patient care through multidisciplinary coll...

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