Utilizing domain knowledge to improve the classification of intravenous contrast phase of CT scans.

Multiple intravenous contrast phases of CT scans are commonly used in clinical practice to facilitate disease diagnosis. However, contrast phase information is commonly missing or incorrect due to discrepancies in CT series descriptions and imaging practices. This work aims to develop a classification algorithm to automatically determine the contrast phase of a CT scan.

Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day Questions.

Background GPT-4V (GPT-4 with vision, ChatGPT; OpenAI) has shown impressive performance in several medical assessments. However, few studies have assessed its performance in interpreting radiologic images. Purpose To assess and compare the accuracy of GPT-4V in assessing radiologic cases with both images and textual context to that of radiologists and residents, to assess if GPT-4V assistance improves human accuracy, and to assess and compare the accuracy of GPT-4V with that of image-only or text-only inputs.

We have to go back, back to the future! Reflecting on 75 years of human factors in the UK to shape a future of responsible artificial intelligence innovation.

The origins of Human Factors (HF) are rooted in the Second World War. It is a sign of the times that 75 years on from the formation of the Ergonomics Research Society, discussions occur as to whether Artificial Intelligence (AI) could/should be capable of controlling weaponry in a theatre of war. HF can support the design of safe, ethical, and usable AI: but there is little evidence of HF influencing industrial organisations developing AI.

Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study.

Purpose: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.

Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation.

Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. In our previous work, a dual-branch network was developed to segment single-class edematous adipose tissue.