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Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21st century. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these changes due to our large digital data footprint. Radiomics is transforming medical images into mineable high‐dimensional data to optimise clinical decision‐making; however, some would argue that AI could infiltrate workplaces with very few ethical checks and balances. In this commentary article, we describe how AI is beginning to change medical imaging services and the innovations that are on the horizon. We explore how AI and its various forms, including machine learning, will challenge the way medical imaging is delivered from workflow, image acquisition, image registration to interpretation. Diagnostic radiographers will need to learn to work alongside our ‘virtual colleagues’, and we argue that there are vital changes to entry and advanced curricula together with national professional capabilities to ensure machine‐learning tools are used in the safest and most effective manner for our patients. This article summarises the opportunities for artificial intelligence (AI) translation into medical imaging in the near future. The changing roles for diagnostic radiographers are explored, and a discussion of the challenges for the ethical implementation of AI is included.

Previous studies showed that radiologists can detect the gist of an abnormality in a mammogram based on a half-second image presentation through global processing of screening mammograms. This study investigated the intra- and inter-observer reliability of the radiologists’ initial impressions about the abnormality (or \"gist signal\"). It also examined if a subset of radiologists produced more reliable and accurate gist signals. Thirty-nine radiologists provided their initial impressions on two separate occasions, viewing each mammogram for half a second each time. The intra-class correlation (ICC) values showed poor to moderate intra-reader reliability. Only 13 radiologists had an ICC of 0.6 or above, which is considered the minimum standard for reliability, and only three radiologists had an ICC exceeding 0.7. The median value for the weighted Cohen’s Kappa was 0.478 (interquartile range = 0.419–0.555). The Mann-Whitney U-test showed that the \"Gist Experts\", defined as those who outperformed others, had significantly higher ICC values (p = 0.002) and weighted Cohen’s Kappa scores (p = 0.026). However, even for these experts, the intra-radiologist agreements were not strong, as an ICC of at least 0.75 indicates good reliability and the signal from none of the readers reached this level of reliability as determined by ICC values. The inter-reader reliability of the gist signal was poor, with an ICC score of 0.31 (CI = 0.26–0.37). The Fleiss Kappa score of 0.106 (CI = 0.105–0.106), indicating only slight inter-reader agreement, confirms the findings from the ICC analysis. The intra- and inter-reader reliability analysis showed that the radiologists’ initial impressions are not reliable signals. In particular, the absence of an abnormal gist does not reliably signal a normal case, so radiologists should keep searching. This highlights the importance of \"discovery scanning,\" or coarse screening to detect potential targets before ending the visual search.

This study presents a quantitative analysis of global breast imaging research over the last eight decades. A dedicated Sino-Australian case study via a social network analysis (SNA) is included as China and Australia have a recent rapidly increasing number of research partnerships and strategic education/economic connections. Bibliographic data was extracted via Scopus and analysed for the social network parameters of degree centrality, closeness centrality, betweenness centrality and multiple cohesion measures in order to explore research collaboration networks at the organisational level. Within the last three decades there has been a tremendous increase in the publication rate within the scientific domain of breast imaging research, however, there is a significant lag in the development of this research area in China compared with Australia. Breast imaging research in China is considerably more insular, with less international collaboration and reduced variation between collaborators than Australia. The impact of national breast screening programs and novel cancer technologies upon collaboration networks is discussed alongside the ability of networks paradigm to reveal both frailties in research connections and to highlight networking strategies.

Breast cancer is the most common neoplasm diagnosed amongst women worldwide and is the leading cause of female cancer death. However, breast cancer in China is not comprehensively understood compared with Westernised countries, although the 5-year prevalence statistics indicate that approximately 11 % of worldwide breast cancer occurs in China and that the incidence has increased rapidly in recent decades. This paper reviews the descriptive epidemiology of Chinese breast cancer in terms of incidence, mortality, survival and prevalence, and explores relevant factors such as age of manifestation and geographic locations. The statistics are compared with data from the Westernised world with particular emphasis on the United States and Australia. Potential causal agents responsible for differences in breast cancer epidemiology between Chinese and other populations are also explored. The need to minimise variability and discrepancies in methods of data acquisition, analysis and presentation is highlighted.

It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P  < 0.05), lesion sensitivity (+ 18.7%; P  < 0.01) and JAFROC (+ 11%; P  < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P  < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers’ weakness and strength are identified.

The information captured by the gist signal, which refers to radiologists’ first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 ( p  = 0.82, CI: −0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 ( p  < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62–0.86), 0.79 (CI: 0.63–0.89), and 0.88 (CI: 0.79–0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.

Breast cancer is the most common occurring cancer in women worldwide. To guide current breast cancer screening program, the level of knowledge about breast cancer should be evaluated. This study aims to ascertain the level of breast cancer knowledge including risk factors, signs and symptoms, and early detection methods, especially mammography screening. The study was conducted among 1353 Jordanian women from the public using a self-administered questionnaire. Responses to the knowledge test were summed for an overall knowledge score. Comparisons between socio-demographics and knowledge, attitude and practice were also measured using a Chi-square test. About 76% of participants were aware that breast cancer is the most common cancer among women in Jordan. About 53.7% of our participants were rated as having an intermediate level of knowledge regarding risk factors, and 44% were rated as having a good to an excellent level of knowledge about breast cancer signs and symptoms. The participants' level of education was the main factor identified as influencing the participants' knowledge of the risk factors, signs and symptoms, and knowledge of early detection methods of breast cancer. The study sample was rated as having an intermediate level of knowledge regarding mammography screening; however, their participation in this screening method was low. Although Jordanian women had an acceptable level of knowledge of breast cancer, the screening rates for mammography were low. These findings suggest that there is a need to provide extra awareness programs for Jordanian women to improve their breast cancer knowledge and practice.

Background Plain radiography is a key diagnostic tool for trauma patients in emergency departments, often requiring immediate interpretation so that urgent care is not delayed. Due to difficulty in accessing timely radiologist reports and the demand for rapid decision-making, emergency department doctors, including junior doctors, have, over the years, been involved in the initial interpretation of plain trauma X-rays. However, concerns remain about the accuracy of these junior doctors, which may impact patient safety. Despite its significance, there’s a notable gap in knowledge on the factors that influence their accuracy and strategies to improve their accuracy. This review explored these specific factors and strategies. Method A scoping review was conducted following the framework by Arksey and O’Malley as updated by Levac, Colquhoun, and O’Brien. Searches were performed in PubMed, SCOPUS, Embase, Cochrane Library, Google Scholar and through reference list search of eligible studies from a timeframe of 1985 to August 2025. A narrative approach was employed to describe findings after content analysis of eligible studies. Results Nine articles were ultimately included. The factors identified were emergency department clinical experience, anatomical site-specific interpretation, radiographic image-related factors, time and mechanism of traumatic injury. Further, plain trauma X-ray interpretation training, emergency department clinical experience with a teaching programme and collaboration with radiographers were identified as potential accuracy improvement strategies. Conclusion Junior doctors’ plain trauma X-ray interpretation accuracy was influenced by several factors. Strategies like training, increased exposure to trauma X-rays in the emergency department with structured teaching programmes, and enhanced collaboration with radiographers can help mitigate the risk of misinterpretations among junior doctors. Future studies should not only validate these findings and investigate additional influencing factors and strategies, but also examine potential barriers to implementing such strategies. Clinical trial number Not applicable.

Background Plain radiography remains a first-line assessment tool for emergency departments’ trauma patients. Given the urgency of trauma care, emergency department doctors, including junior doctors, often perform initial trauma X-ray interpretations to support timely patient management when there is an unavailability of an immediate radiologist report. However, trauma X-ray interpretation is challenging, and inaccuracies can impact patient care. This study evaluates the diagnostic accuracy of emergency department junior doctors on the initial interpretation of trauma X-rays by systematically reviewing and meta-analysing existing research on the subject. Method Studies were identified from PubMed, Scopus, Embase, Cochrane Library, and by checking the reference lists of relevant studies. Quality assessment of included studies was evaluated using the QUADAS-2 tool. Meta-analysis was conducted using bivariate models, with summary estimates reported as sensitivity, specificity, and the SROC. Meta-regression and subgroup analysis was performed to evaluate the sources of heterogeneity. Publication bias was assessed using Deeks’ funnel plot. Results Seven studies were included in this meta-analysis. Across the studies, pooled sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were 0.65 (95% CI = 0.47–0.80), 0.89 (95% CI = 0.77–0.95), and 0.86 (95% CI = 0.83–0.89), respectively. Covariate analysis per anatomical region of trauma X-rays showed that for skeletal region, pooled sensitivity, specificity, and AUC were 0.72 (95% CI = 0.55–0.85), 0.86 (95% CI = 0.73–0.93), and 0.87 (95% CI = 0.83–0.89), respectively and for appendicular region, pooled sensitivity, specificity and AUC were 0.68 (95% CI = 0.49–0.82), 0.82 (95% CI = 0.62–0.93), and 0.81 (95% CI = 0.77–0.84), respectively. Substantial heterogeneity was identified but was not due to a threshold effect (Spearman rho = 0.29( p  = 0.49)). Meta-regression and subgroup analysis revealed that anatomical-region-specific trauma X-ray interpretation and accuracy assessment techniques influenced heterogeneity. No publication bias was identified ( p  = 0.41). Conclusion Emergency department junior doctors’ accuracy in the initial interpretation of trauma X-rays was moderate. The findings further suggest a high likelihood of missed abnormalities when they interpret trauma X-rays. This highlights the need for support strategies to enhance their diagnostic accuracy to strengthen clinical decision-making in trauma care. Clinical trial number Not applicable.

Breast cancer is one of the most commonly diagnosed malignancies and the leading cause of cancer death of women over the world. A large number of females with breast cancer in Vietnam and other Southeast Asian （SEA） countries present at an early age with more aggressive tumors compared with women in Australia. Despite experiencing a low incidence rate, the increasing incidence rate among SEA countries exceeds that of the Westernized world. Changes in reproductive factors, environmental exposures, and lifestyle are the possible causes of this trend. However, limited evidence shows that these factors are associated with breast cancer in the Vietnamese population. Breast cancer incidence rates within Vietnam are not uniform and appear to be dependent on geographic location. Findings from this review have important implications for breast cancer control and treatment in Vietnam. A good understanding of the morphology of the breast and the type and nature of breast cancers presenting in Vietnam is required to facilitate the introduction of an effective national breast screening program.