Diagnostic Radiology

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 diagnosis, treatment, and patient management by bridging the gap between radiologists and referring physicians. However, the manual generation of radiology reports is time-consuming and labor-intensive, leading to inefficiencies and delays in clinical workflows, particularly as case volumes increase. Al...

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 treatment. Although most radiology reports are accurate, errors can occur in the final reports due to high workloads, use of dictation software, and human error. Advanced artificial intelligence models, such as GPT-4, show potential as tools to improve report accuracy. This retrospective study evalua...

Fourier ptychography microscopy for digital pathology.

Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high resolution, and ...

Large models in medical imaging: Advances and prospects.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large languag...

Canadian Radiology Update.

Radiology research at Canadian institutions is advancing patient care through multidisciplinary collaboration, technological innovation, and quality i...

Artificial Intelligence in Dental Imaging Practice among Oral Radiologists from the City of Chennai: A Qualitative Analysis.

BACKGROUND: Artificial intelligence (AI) has the potential to significantly transform the role of the doctor and revolutionize the practice of medicin...

Artificial Intelligence for Head and Neck Squamous Cell Carcinoma: From Diagnosis to Treatment.

Head and neck squamous cell carcinoma (HNSCC) remains a globally prevalent malignancy with high morbidity and mortality. Despite therapeutic advances,...

Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 2: Analysis of DALL-E 3.

Disparity among gender and ethnicity remains an issue across medicine and health science. Only 26%-35% of trainee radiologists are female, despite mor...

RadCLIP: Enhancing Radiologic Image Analysis Through Contrastive Language-Image Pretraining.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted ...

ESR Essentials: a step-by-step guide of segmentation for radiologists-practice recommendations by the European Society of Medical Imaging Informatics.

High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent ro...

Diagnostic Methods and Biomarkers in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) refers to a chronic inflammatory condition involving the GI tract that includes Crohn's disease (CD) and ulcerative c...

Neuroimaging Characterization of Acute Traumatic Brain Injury with Focus on Frontline Clinicians: Recommendations from the 2024 National Institute of Neurological Disorders and Stroke Traumatic Brain Injury Classification and Nomenclature Initiative Imaging Working Group.

Neuroimaging screening and surveillance is one of the first frontline diagnostic tools leveraged in the acute assessment (first 24 h postinjury) of pa...

Ethical framework for responsible foundational models in medical imaging.

The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnos...

Benchmarking Radiology Report Generation From Noisy Free-Texts.

Automatic radiology report generation can enhance diagnostic efficiency and accuracy. However, clean open-source imaging scan-report pairs are limited...

From Image to Diagnosis: Convolutional Neural Networks in Tongue Lesions.

Clinical examination of the tongue is essential for diagnosing systemic and local diseases. However, traditional diagnostic methods rely on subjective...

The Role of Large Language Models (LLMs) in Breast Imaging Today and in the Near Future.

This narrative review focuses on the integration of large language models (LLMs), such as GPT-4 and Gemini, into breast imaging. LLMs excel in underst...

FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review.

PURPOSE: This review aims to provide a comprehensive overview of the transformative impact of FDA-approved artificial intelligence (AI) products in ab...

A systematic literature review: exploring the challenges of ensemble model for medical imaging.

BACKGROUND: Medical imaging has been essential and has provided clinicians with useful information about the human body to diagnose various health iss...

Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.

This article explores the characteristics and scope of artificial intelligence (AI) competitions in medical imaging. A retrospective evaluation of AI ...

MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.

Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis an...