Introduction: Non‐small cell lung cancer (NSCLC) is the leading cause of cancer‐related mortality worldwide. Despite advancements in early detection and treatment, postsurgical recurrence remains a significant challenge, occurring in 30%–55% of patients within 5 years after surgery. This review analysed existing studies on the utilisation of artificial intelligence (AI), incorporating CT, PET, and clinical data, for predicting recurrence risk in early‐stage NSCLCs.Methods: A literature search was conducted across multiple databases, focusing on studies published between 2018 and 2024 that employed radiomics, machine learning, and deep learning based on preoperative positron emission tomography (PET), computed tomography (CT), and PET/CT, with or without clinical data integration. Sixteen studies met the inclusion criteria and were assessed for methodological quality using the METhodological RadiomICs Score (METRICS).Results: The reviewed studies demonstrated the potential of radiomics and AI models in predicting postoperative recurrence risk. Various approaches showed promising results, including handcrafted radiomics features, deep learning models, and multimodal models combining different imaging modalities with clinical data. However, several challenges and limitations were identified, such as small sample sizes, lack of external validation, interpretability issues, and the need for effective multimodal imaging techniques.Conclusions: Future research should focus on conducting larger, prospective, multicentre studies, improving data integration and interpretability, enhancing the fusion of imaging modalities, assessing clinical utility, standardising methodologies, and fostering collaboration among researchers and institutions. Addressing these aspects will advance the development of robust and generalizable AI models for predicting postsurgical recurrence risk in early‐stage NSCLC, ultimately improving patient care and outcomes.

Introduction: Image reject analysis (RA) in direct digital radiography (DDR) is an important quality indicator tool. Analysis of rejected images is a component of quality assurance (QA) programmes, with the overall aim of reducing patient radiation dose. This study aimed to compare differences in image rejection rates (RR) and the reasons for rejection between two radiology departments.Methods: A retrospective quantitative descriptive study of images performed across the two radiology departments (RAD 1 and RAD 2) acquired with DDR systems between the beginning of February and the end of May 2021 was undertaken. Collected data included the medical imaging technologist (MIT) selection of image rejection reasons for different anatomic regions and compared between the two radiology departments.Results: A total of 47,046 images and 29,279 images were acquired at RAD 1 and RAD 2, respectively, with an overall image rejection rate of 7.86% at RAD 1 and 5.91% at RAD 2. The primary reason for image rejections was positioning errors, 79.4% and 77.3% recorded at RAD 1 and RAD 2, respectively. Significant differences were demonstrated between the two radiology departments for image rejection rates and selected reasons for rejection for most anatomical body groups.Conclusion: The implementation of image RA remains a key part of QA in radiology departments utilising DDR systems. This study recommends interventions based on image RRs for examinations taking into consideration the department‐specific variations and imaging protocols used.

African countries face a critical challenge as radiography professionals and other healthcare workers migrate to high-income countries with better-resourced health systems. This study sought to identify the factors influencing the migration of qualified radiography professionals and evaluate the challenges and strategies for retaining the radiography workforce across the continent. This quantitative cross-sectional study was conducted using an online questionnaire. The link to the questionnaire was widely advertised through professional body platforms, as well as the professional and social media pages of radiography professionals across Africa. Descriptive, inferential statistics and exploratory factor analysis using principal component analysis were used to examine the data. A total of 755 radiography professionals from 17 African countries participated. Poor salary was the most commonly cited push factor for migration, reported by 80.00 % of participants. In the exploratory factor analysis, five factors were identified, explaining 55.57 % of the variation in the data on push factors for migration among radiography professionals. These factors include the health system, economic concerns, professional challenges, political issues, and social conditions. Better salaries (86.49 %) and improved living conditions (86.35 %) were the most common pull factors. Additionally, 92.00 % of participants identified improving radiography professionals' salaries as the most effective strategy for reducing migration. Low salaries, inadequate working conditions, and the pursuit of better opportunities abroad are key contributors to the migration of radiography professionals from Africa. To address this issue, African healthcare systems must prioritise improving remuneration, enhancing working conditions, investing in health infrastructure, and implementing targeted policies focused on professional development and career growth. Retaining radiography professionals in Africa requires a multifaceted approach to strengthen the workforce and improve healthcare systems.

Research remains one of the essential elements that drive the growth of a profession since it contributes to the development of knowledge. Research also creates value for public service and leads to better health outcomes. Research output from the Medical Imaging and Radiation Sciences (MIRS) profession is currently lacking in New Zealand while the output from Australia can also be improved. For research to be improved, there needs to be intentionality. This commentary presents proposed considerations that could contribute toward increasing research impact and research output in the MIRS profession in Australia and New Zealand.