Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21^st 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.

Obesity is a global health issue with obese patients requiring specialised diagnosis, treatment and care through the health service. The practical and social difficulties associated with medical imaging of obese patients are an increasingly common problem and it is currently unknown how student and qualified radiographers perceive and respond to these challenges. By better understanding challenges presented in providing quality imaging and care of imaging obese patients, education for both qualified and student radiographers can be enhanced. Radiographers are heavily reliant on visual and tactile senses to locate the position of anatomical structures for diagnostic imaging and determine radiation exposure through a delicate consideration of dose, image quality and anatomical attenuation. However, obese patients require modifications to routine radiographic practice in terms of movement/assisted positioning, equipment capabilities to take increased weight or coverage. These patients may also be subject to compromised radiological diagnosis through poor visualisation of structures. In this paper, the professional and educational literature was narratively reviewed to assess gaps in the evidence base related to the skill and care knowledge for obese patients. Literature was sourced relating to discrete radiographic considerations such as the technical factors of imaging obese patients, exposure and the impact of obesity on imaging departments’ service provisions. The recent literature (post‐2000 to coincide with the sharp increase in global obesity) on the perceptions of health professionals and student health practitioners has also been explored because there are no specific radiographer studies to report. By understanding the research in similar fields, we may identify what common attitudes qualified and student radiographer's hold and what challenges, technical and care related, can be prepared for.

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners’ unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

The Imaging and Medical Beamline (IMBL) is a superconducting multipole wiggler-based beamline at the 3 GeV Australian Synchrotron operated by the Australian Nuclear Science and Technology Organisation (ANSTO). The beamline delivers hard X-rays in the 25–120 keV energy range and offers the potential for a range of biomedical X-ray applications, including radiotherapy and medical imaging experiments. One of the imaging modalities available at IMBL is propagation-based X-ray phase-contrast computed tomography (PCT). PCT produces superior results when imaging low-density materials such as soft tissue (e.g., breast mastectomies) and has the potential to be developed into a valuable medical imaging tool. We anticipate that PCT will be utilized for medical breast imaging in the near future with the advantage that it could provide better contrast than conventional X-ray absorption imaging. The unique properties of synchrotron X-ray sources such as high coherence, energy tunability, and high brightness are particularly well-suited for generating PCT data using very short exposure times on the order of less than 1 min. The coherence of synchrotron radiation allows for phase-contrast imaging with superior sensitivity to small differences in soft-tissue density. Here we also compare the results of PCT using two different detectors, as these unique source characteristics need to be complemented with a highly efficient detector. Moreover, the application of phase retrieval for PCT image reconstruction enables the use of noisier images, potentially significantly reducing the total dose received by patients during acquisition. This work is part of ongoing research into innovative tomographic methods aimed at the introduction of 3D X-ray medical imaging at the IMBL to improve the detection and diagnosis of breast cancer. Major progress in this area at the IMBL includes the characterization of a large number of mastectomy samples, both normal and cancerous, which have been scanned at clinically acceptable radiation dose levels and evaluated by expert radiologists with respect to both image quality and cancer diagnosis.