AI Medical Compendium Journal:
Clinical imaging

Showing 1 to 10 of 64 articles

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-...

A review of explainable AI techniques and their evaluation in mammography for breast cancer screening.

Clinical imaging
Explainable AI (XAI) methods are gaining prominence in medical imaging, addressing the critical need for transparency and trust in AI-driven diagnostic tools. Mammography, as the cornerstone of early breast cancer detection, holds immense potential f...

The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study.

Clinical imaging
BACKGROUND: Vestibular schwannomas (VS) are benign tumors that can lead to hearing loss, balance issues, and tinnitus. Gamma Knife Radiosurgery (GKS) is a common treatment for VS, aimed at halting tumor growth and preserving neurological function. Ac...

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...

Radiomics and machine learning models for diagnosing microvascular invasion in cholangiocarcinoma: a systematic review and meta-analysis of diagnostic test accuracy studies.

Clinical imaging
PURPOSE: We aimed to systematically assess the value of radiomics/machine learning (ML) models for diagnosing microvascular invasion (MVI) in patients with cholangiocarcinoma (CCA) using various radiologic modalities.

MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.

Clinical imaging
We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning (DL) models in predicting glioma alpha thalassemia/mental retardation syndrome X-linked (ATRX) stat...

Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models.

Clinical imaging
BACKGROUND: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for recurrence and survival. Radiomics and deep learning (DL) advancements offer the potential for improved...

Inclusive AI for radiology: Optimising ChatGPT-4 with advanced prompt engineering.

Clinical imaging
This letter responds to the article "Encouragement vs. liability: How prompt engineering influences ChatGPT-4's radiology exam performance," offering additional perspectives on optimising ChatGPT-4 for Radiology applications. While the study highligh...