Radiography is arguably one of the most technologically advanced health care disciplines, encompassing various technologies that continuously evolve with advancements in computing power and human knowledge [1,2]. Although the use of technology has always been extensive in the profession, recent technological advancements have focused on incorporating complex machine learning algorithms, which have led to a change in clinical protocols that affect patient outcomes and radiography practice [3-5]. Artificial intelligence (AI) technologies play an increasingly important role in clinical radiography with applications such as scheduling patients, image interpretation and reporting, vetting of examinations, patient positioning, image generation and reconstruction, radiation therapy dosimetry, and image postprocessing [4,6-8]. As the number of AI applications increases in clinical practice [9], it is postulated that the practitioner role may change and expand, and these new roles will require modifications to the competencies and educational requirements of current and future practitioners [4].

The increasing integration of AI technologies in radiography practice raises significant questions about the influences of AI technology on radiography practice and the subsequent ways in which radiography education may be required to change. Recent reviews have explored AI’s current and potential applications in radiography practice [5] and examined the AI educational programs available to radiography staff globally [1]. Furthermore, research undertaken in the United Kingdom, Africa, and the Middle East exploring AI topics linked to education in radiography has focused on equipping practitioners for successful AI adoption [10] as well as the perceptions and attitudes of students and practitioners [11-16]. These conversations illustrate that there has been increasing exploration of various facets of AI in radiography, indicating that the emergence of AI technologies will continue to shape the future of radiography and radiography education.

Opinion statements and discussion pieces argue for the inclusion of AI education in undergraduate radiography curricula and appropriate education for current practitioners [3,4,17]. In addition, recommendations were made that higher education institutions must ensure that radiography curricula provide educational opportunities for patient-centered care in relation to AI integration to ensure competent and safe practice [2,3,6]. With increasing automation in clinical tasks, patient-centered care will gain more importance, highlighting the need for academic curricula to prioritize patient-centered care [2,4]. The evolving landscape of radiography, marked by the increasing integration of AI, raises important questions about how to best prepare future radiographers. Therefore, mapping the existing literature on the way that radiography education may need to change because of the integration of AI technologies in clinical practice is needed to grasp the current state of knowledge on the topic for future educational considerations. The aim of this scoping review is to explore and synthesize the literature on the adjustments needed in the radiography curriculum to prepare radiography students for the demands of AI-assisted health care environments.

The final database searches were completed in October 2024 and in total produced 2224 items. Title and abstract screening of 1930 articles is underway after removing the 294 duplicates. The results will be presented in relation to the research question and the objective of the study. Furthermore, the results will be presented in 2 parts as proposed by Peters and colleagues [19]. The first part will consist of the results of the search and the inclusion of the studies will be presented with the PRISMA-ScR flow diagram (Figure 1). The blue arrow indicates the current status. The second part will consist of a narrative description that aligns with the objective of the scoping review. Data analysis and results may be further refined through the review process as the contents of the review are taken into consideration. Finalization of the scoping review is expected by March 2025. The results of the scoping review will be disseminated through publication in an accredited journal.

Addressing the call for an update to the radiography curriculum in response to new technological advancements such as AI is critical to prepare and equip current and future professionals for safe practice. This scoping review will map various studies and is anticipated to provide an overview of the adjustments needed in the radiography curriculum to prepare radiography students for the demands of AI-assisted health care environments. In return, the results will help to inform a variety of stakeholders, including radiography educators, radiography regulatory bodies, and radiography professional groups, to plan for needed changes in radiography education in response to the impact of AI-based technologies on clinical processes. To the best of our knowledge, this is the first scoping review to synthesize existing evidence of how the radiography curriculum might best prepare future practitioners for the demands of AI-assisted health care environments.

A strength of the scoping review is that all available studies of published data will be included. Current available literature focusing on how the radiography curriculum might need to change, considering the incorporation of AI technology in clinical practice, focuses on opinion and discussion pieces, and white papers [4,17,24]. In addition, some studies have highlighted the perceptions of educators and students on the incorporation of AI into radiography curricula; however specific methods of integration and a lack of implementation were noted [1]. Therefore, performing this review is warranted to help provide the necessary insights into changes to the curriculum that are aligned with the advances in technology.

A scoping review explores the breadth and not the depth of a topic, and the reviewers cannot comment on the quality of the studies included in the review. In addition, non-English articles may be excluded from this review, which can potentially lead to missing valuable work. However, for the search strategy, all languages are included and the number of studies for all languages will be documented. This process will show how many studies were identified but not included, promoting transparency in the selection process [22]. Furthermore, where possible translations will be sought to limit the omission of information.

This study will provide valuable insights into how the radiography curriculum should adapt to meet the educational needs of current and future practitioners to ensure competent and safe practice in response to AI technologies. Mapping the existing evidence is essential because the growing integration of AI-based technologies in clinical practice must be supported with appropriate education and training [1,4]. After collating and analyzing the findings of the scoping review, a manuscript containing the final analysis will be written and submitted for publication. In addition, this review will contribute toward a PhD in health professions education.