Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

Artificial intelligence for precision education in radiology - experiences in radiology teaching from a UK foundation doctor.

ObjectivesThe patients’ view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology.MethodsSix domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach’s alpha and composite reliability.ResultsThe exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5).ConclusionsThis study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale.Key Points• Although AI systems are increasingly developed, not much is known about patients’ views on AI in radiology.• Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients’ views on AI in radiology, revealing five factors.• Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.

Objective:To test medical students’ perceptions of the impact of artificial intelligence (AI) on radiology and the influence of these perceptions on their choice of radiology as a lifetime career.Methods:A cross-sectional multicenter survey of medical students in Saudi Arabia was conducted in April 2019.Results:Of the 476 respondents, 34 considered radiology their first specialty choice, 26 considered it their second choice, and 65 considered it their third choice. Only 31% believed that AI would replace radiologists in their lifetime, while 44.8% believed that AI would minimize the number of radiologists needed in the future. Approximately 50% believed they had a good understanding of AI; however, when knowledge of AI was tested using five questions, on average, only 22% of the questions were answered correctly. Among the respondents who ranked radiology as their first choice, 58.8% were anxious about the uncertain impact of AI on radiology. The number of respondents who ranked radiology as one of their top three choices increased by 14 when AI was not a consideration. Radiology conferences and the opinions of radiologists had the most influence on the respondents’ preferences for radiology.Conclusion:The worry that AI might displace radiologists in the future had a negative influence on medical students’ consideration of radiology as a career. Academic radiologists are encouraged to educate their students about AI and its potential impact when students are considering radiology as a lifetime career choice.Advances in knowledge:Rapid advances of AI in radiology will certainly impact the specialty, the concern of AI impact on radiology had negative influence in our participants and investing in AI education and is highly recommended.

Patient Perspectives on Artificial Intelligence in Radiology

Artificial Intelligence (AI) is reshaping all sectors of society, including libraries. AI adoption in libraries has been gradual due to concerns and challenges, including ethical issues, maturity of the technology, insufficient AI education and training designed for library and information professionals, and gaps in AI education in library and information science (LIS) programs. This case study reports on the motivations, processes, and evaluations of the IDEA Institute on AI that was developed to equip two cohorts (Fellows) of information professionals who participated in the 2021 and 2022 IDEA Institute on AI with the foundational knowledge and skills to lead AI work. A multi-method approach was used to collect and analyze the evaluation data from multiple sources at different points of the IDEA Institute on AI. The IDEA Institute on AI applied an outcome-based planning and evaluation model and employed formative and summative evaluations using surveys and focus-group discussions. Fellows worked in various library and information environments, most in academic libraries. The case study results showed that the Fellows’ AI knowledge and skills increased substantially, their confidence greatly increased upon completing the IDEA Institute on AI, and they engaged in AI projects in their workplaces. They built awareness of AI issues and challenges and developed a comprehensive understanding of AI within the context of equity, diversity, inclusion, and accessibility. The Fellows’ supervisors were positive about the learning and experience their Fellows gained from the IDEA Institute on AI and their peers. The results of this case study have significant implications for developing AI professional development programs in the LIS field, providing exemplary AI education and training as AI technology evolves, including generative AI and large language models, and integrating AI into LIS curricula.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine.AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice.This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future.The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

Simple SummaryArtificial Intelligence (AI) has been increasingly used in radiology to improve diagnostic procedures over the past decades. The application of AI at the time of cancer diagnosis also creates challenges in the way doctors should communicate the use of AI to patients. The present systematic review deals with the patient’s psycho-cognitive perspective on AI and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Evidence from the retrieved studies pointed out that the use of AI in radiology is negatively associated with patient trust in AI and patient-centered communication in cancer disease.Background: In the past decade, interest in applying Artificial Intelligence (AI) in radiology to improve diagnostic procedures increased. AI has potential benefits spanning all steps of the imaging chain, from the prescription of diagnostic tests to the communication of test reports. The use of AI in the field of radiology also poses challenges in doctor–patient communication at the time of the diagnosis. This systematic review focuses on the patient role and the interpersonal skills between patients and physicians when AI is implemented in cancer diagnosis communication. Methods: A systematic search was conducted on PubMed, Embase, Medline, Scopus, and PsycNet from 1990 to 2021. The search terms were: (“artificial intelligence” or “intelligence machine”) and “communication” “radiology” and “oncology diagnosis”. The PRISMA guidelines were followed. Results: 517 records were identified, and 5 papers met the inclusion criteria and were analyzed. Most of the articles emphasized the success of the technological support of AI in radiology at the expense of patient trust in AI and patient-centered communication in cancer disease. Practical implications and future guidelines were discussed according to the results. Conclusions: AI has proven to be beneficial in helping clinicians with diagnosis. Future research may improve patients’ trust through adequate information about the advantageous use of AI and an increase in medical compliance with adequate training on doctor–patient diagnosis communication.

While extensive research has examined adult perceptions of Artificial Intelligence (AI)’s impact on the workforce, studies focusing on adolescents—who are at a critical stage of career development—remain scarce. Drawing upon Social Cognitive Career Theory (SCCT) and the significance of fostering adolescents’ adaptability in the era of AI, we designed a questionnaire-based experiment to examine how adolescents’ prior perceptions, attitude and immediate knowledge of AI influence their career preferences. We conducted a questionnaire-based experiment with 836 adolescents (Mage = 13.98, SD = 1.35 years; 52.30% male) to investigate the influence of three independent variable groups: prior perception of AI (familiarity with AI), prior attitudes towards AI (trust in AI and positive/negative attitude towards AI) and immediate knowledge of AI (experimental manipulation). The results showed that immediate knowledge of AI significantly and negatively predicted adolescents’ preference for both low- and high-replaceable jobs. In contrast, familiarity with AI, trust in AI and positive attitude towards AI significantly and positively predicted a preference for low-replaceable jobs. This study introduces an integrated empirical framework demonstrating that distinct AI-related factors differentially influence adolescent career preferences. Results emphasize the importance of familiarity with AI, trust in AI and positive attitude towards AI among teenagers in order to better adapt the future changes in the labor market. This finding provides an empirical reference for educators and policy makers to better guide teenagers to make career plans.

This research explores the relationship between Artificial Intelligence (AI) literacy and trust in AI systems. As AI continues to be integrated into various aspects of daily life, understanding the connection between knowledge of AI and trust is crucial. The study develops a comprehensive AI literacy questionnaire, addressing key dimensions such as "Know and Understand AI," "Use and Apply AI," and "Evaluate and Create AI," alongside questions measuring the degree of trust in AI. The research finds a weak negative relationship between AI literacy and trust, which is different from existing literature that typically suggests a positive or neutral correlation. The results, though not statistically significant, highlight the importance of AI education and its potential role in shaping public trust. Despite limitations such as sample size and time constraints, the study offers valuable insights and contributes to the ongoing dialogue about AI literacy and trust. Future research, with a larger and more diverse sample, could help clarify these findings and further explore the interplay between AI literacy and trust across different populations and settings.

BackgroundArtificial intelligence has become an integral part of modern radiology, improving diagnostic accuracy, workflow efficiency, and decision-making processes. However, the acceptance and effective use of artificial intelligence in healthcare largely depends on healthcare professionals’ perceptions and literacy regarding these technologies. The aim of this study was to develop and validate the “Perception Scale for Artificial Intelligence in Radiologic Imaging” and to examine the factors that influence healthcare professionals’ perceptions of artificial intelligence in radiology. It also aimed to determine healthcare professionals’ perceptions regarding the use of artificial intelligence in radiology and to examine the factors that influence these perceptions, particularly the role of artificial intelligence literacy.MethodsThis cross-sectional, questionnaire-based study was conducted between March and May 2025 among healthcare professionals working in public and private hospitals in Turkey. Data were collected from 425 participants using convenience sampling. The “Perception Scale for Artificial Intelligence in Radiologic Imaging” was developed for this study, and the “Artificial Intelligence Literacy Scale” was employed to test contextual validity. Validity and reliability were evaluated using Cronbach’s Alpha, and analyses were performed with parametric tests in SPSS 26.0 and AMOS 24.ResultsThe Perception of Artificial Intelligence in Radiologic Imaging Scale demonstrated a valid three-dimensional structure with 14 items and high reliability. The mean perception score of healthcare professionals regarding artificial intelligence in radiologic imaging was 3.14 ± 0.66 (mean ± standard deviation), indicating a moderate level of perception. A significant positive correlation was observed between artificial intelligence literacy and perception (r = 0.270, p < 0.001), while no significant differences were found across demographic variables (p > 0.05).ConclusionThe study highlights that healthcare professionals in Turkey hold a moderately positive perception of artificial intelligence use in radiology. Furthermore, higher artificial intelligence literacy levels are associated with more favorable perceptions. These findings emphasize the need for educational initiatives to improve artificial intelligence literacy and foster informed, confident adoption of artificial intelligence technologies in clinical radiology practice.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12909-025-08392-0.

Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse for international medical graduates (IMGs). Using the Artificial Intelligence in Radiology Education (AIRE) curriculum, we assessed the radiology AI course's effectiveness in increasing the AI competency of IMGs. AIRE curriculum features nine free YouTube lectures on AI in radiology. Participants watched lectures remotely on fundamental AI terms, methods, clinical applications and special topics. They completed a pre- and post-course e-survey and assessment. The survey assessed participants' prior AI experience, subjective knowledge and opinions on the need for AI in medical education. The assessment determined participants' knowledge of AI. Pre- and post-course assessment scores were compared using a Student's t-test to determine if the course increased participant knowledge of AI terms and applications. Three hundred fifty-seven students from 28 countries enrolled in the course; 175 completed the course within the study period. Nearly all participants reported insufficient AI exposure in their radiology training (91.3%). Participants' knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course assessment score of 6.5/15 and a post-course assessment score of 9.4/15 (p < 0.0001). AIRE curriculum's effectiveness demonstrates that a remote education course is a viable model to bring accessible fundamental AI education to international medical students. Remote education curricula in medical AI can help mitigate disparities in AI education for lower resource medical programmes.

Objective: The study aimed to identify the attitudes of music skills teachers in Oman towards the use of artificial intelligence (AI) in teaching and to understand the challenges they face in integrating AI into their teaching methods. Theoretical Framework: The proliferation of modern learning resources and technological advancements, especially the emergence of artificial intelligence, have significantly impacted educational institutions. Oman is striving to align its education system with the digital transformation outlined in Oman Vision 2040, which emphasizes high-quality education, the integration of technology, innovation, entrepreneurship, and skill-building. Method: This study adopted a descriptive analytical method, analyzing a questionnaire completed by music skills teachers in Oman regarding their opinions on AI in teaching. The survey focused on three main axes: knowledge of AI, its use in teaching music skills, and the challenges faced by teachers in integrating AI into their practice. Results and Discussion: The results indicated that music skills teachers in Oman have a positive attitude towards the use of artificial intelligence in teaching. Regarding knowledge of AI, the statement "I strive to develop my knowledge about artificial intelligence and its applications in various fields" received the highest mean score of 4.14 with a standard deviation of 0.808, showing a strong commitment to learning about AI. In terms of using AI in teaching music skills, the statement "I intend to use artificial intelligence technologies in the future" ranked highest with a mean score of 4.24 and a standard deviation of 0.761, reflecting a positive intention to integrate AI into their future teaching practices. However, when it came to challenges, the statement "Lack of specialized training in artificial intelligence for music skills teachers" emerged as the most significant barrier, with the highest mean of 4.26 and a standard deviation of 0.782, pointing to the lack of adequate training as the primary obstacle in AI adoption for teaching music skills. Conclusion: The study concludes that music skills teachers in Oman generally have positive attitudes toward the use of AI in teaching. However, the main challenge identified was the lack of specialized training for teachers, which hinders the effective implementation of AI in music education. Research Implications: This research highlights the need for better training and resources for teachers to effectively incorporate AI in their teaching practices. It also emphasizes the importance of curriculum updates to align with AI advancements. Originality/Value: This study provides valuable insights into the perspectives of music skills teachers in Oman on the integration of AI in teaching, contributing to the growing body of research on AI in education in the Middle East. Contributions: The study contributes to the understanding of the role of AI in music education, specifically in Oman, and identifies key barriers that must be addressed for effective integration.