Radiology-pathology correlation is essential for multidisciplinary collaboration in diagnosis and understanding the mechanism of CNS damage in infectious processes. The microscopic acute inflammatory processes are well established and are supplemented by a variety of less-invasive microbial and immunohistochemical investigations. Understanding the pathogenesis of pathogen spread and neuroinvasion, vascular and immune-mediated brain, and spinal cord damage are essential for interpreting radiological images.
The amount of acquired radiology imaging studies grows worldwide at a rapid pace. Novel information technology tools for radiologists promise an increase of reporting quality and as well quantity at the same time. Automated text report drafting is one branch of this development. We defined for the present study in total 9 cases of distal radius fracture. Command files structured according to a template of the Radiological Society of North America (RSNA) and to Arbeitsgemeinschaft Osteosynthese (AO) classifiers were given as input to the natural language processing tool ChatGPT. ChatGPT was tasked with drafting an appropriate radiology report. A parameter study (n = 5 iterations) was performed. An overall high appraisal of ChatGPT radiology report quality was obtained in a score card based assessment. ChatGPT demonstrates the capability to adjust output files in response to minor changes in input command files. Existing shortcomings were found in technical terminology and medical interpretation of findings. Text drafting tools might well support work of radiologists in the future. They would allow a radiologist to focus time on the observation of image details and patient pathology. ChatGPT can be considered a substantial step forward towards that aim.
The inherent biological hazards associated with ionizing radiation necessitate the implementation of effective shielding measures, particularly in medical applications. Interventional radiology, in particular, poses a unique challenge as it often exposes medical personnel to prolonged periods of high x-ray doses. Historically, lead and lead-based compounds have been the primary materials employed for shielding against photons. However, the drawbacks of lead, including its substantial weight causing personnel's inflexibility and its toxicity, have raised concerns regarding its long-term impact on both human health and the environment. Barium tantalate has emerged as a promising alternative, due to its unique attenuation properties against low-energy x-rays, specifically targeting the weak absorption area of lead. In the present study, we employ the Geant4 Monte Carlo simulation tool to investigate various formulations of barium tantalate doped with rare earth elements. The aim is to identify the optimal composition for shielding x-rays in the context of interventional radiology. To achieve this, we employ a reference x-ray spectrum typical of interventional radiology procedures, with energies extending up to 90 keV, within a carefully designed simulation setup. Our primary performance indicator is the reduction in air kerma transmission. Furthermore, we assess the absorbed doses to critical organs at risk within a standard human body phantom protected by the shield. Our results demonstrate that specific concentrations of the examined rare earth impurities can enhance the shielding performance of barium tantalate. To mitigate x-ray exposure in interventional radiology, our analysis reveals that the most effective shielding performance is achieved when using barium tantalate compositions containing 15% Erbium or 10% Samarium by weight. These findings suggest the possibility of developing lead-free shielding solutions or apron for interventional radiology personnel, offering a remarkable reduction in weight (exceeding 30%) while maintaining shielding performance at levels comparable to traditional lead-based materials.
Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.
The author recently spent 4 months at the University of Malaya in Kuala Lumpur, Malaysia, as a visiting professor of radiology. In this report he describes his experiences in the country and at the hospital and medical school, which is one of only three in Malaysia. Each day, the author worked one-on-one with a different resident, supervising either procedures or image interpretation. Additional activities included conferences, grand rounds, and medical student teaching. Because of the mixed ethnicity of the population, the incidence and variety of pathologic conditions that were encountered clinically provided an unparalleled educational experience. The author encourages anyone with available time to consider a sabbatical such as this; it is a rewarding personal and professional experience.