Artificial intelligence in radiology.

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.

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.

BACKGROUND Background: Integration of artificial intelligence (AI) in radiology has advanced significantly but research on how it affects the daily work of radiology staff is limited. This study aimed to explore the experiences of radiology staff on the integration of an AI application in a radiology department in Sweden. This understanding is essential for developing strategies to address potential challenges in AI integration and optimize the use of AI applications in radiology practice. OBJECTIVE Objective: The aim of this study was to explore the experiences of radiology staff on the integration of an AI application in a radiology department in Sweden. The study seeks to provide insights into the real-world application of AI in radiology, with a particular focus on understanding the practical implications of AI integration based on the experiences of staff in a radiology department. METHODS Methods: A study on the integration of AI-powered medical imaging software designed to assist radiologists in analyzing and interpreting medical images was conducted in a radiology department with 40 employees at a hospital in southwestern Sweden. Using a qualitative design, interviews were conducted with 7 radiologists (physicians), 4 radiologic technologists, and 1 physician assistant. Their experience within radiology varied between 13 months and 38 years. The data were analyzed using qualitative content analysis. RESULTS Results: Participants cited numerous strengths and advantages of significant value in integrating AI into radiology practice. Numerous challenges were also revealed in terms of difficulties associated with choosing, acquiring, and deploying the AI application and operational issues in radiology practice. They discussed experiences with diverse strategies to facilitate the integration of AI in radiology to address various challenges and problems. CONCLUSIONS Conclusions: Radiology staff in Sweden benefited from AI integration, enhancing decision-making and quality of care. However, they encountered challenges from pre-adoption to routine use of AI in radiology practice. Strategies such as internal training and workflow adaptation can facilitate successful integration of AI in radiology. CLINICALTRIAL No

Focus issue: Artificial intelligence in medical physics.

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.

Artificial Intelligence in Radiology: What Is Its True Role at Present, and Where Is the Evidence?

BackgroundThe integration of artificial intelligence (AI) in radiology has advanced significantly, but research on how it affects the daily work of radiology staff is limited.ObjectiveThis study aimed to explore the experiences of radiology staff on the integration of an AI application in a radiology department in Sweden. This understanding is essential for developing strategies to address potential challenges in AI integration and optimize the use of AI applications in radiology practice.MethodsThis qualitative case study was conducted in a single radiology department with 40 employees in 1 hospital in southwestern Sweden. The study concerned the integration of an AI-powered medical imaging software designed to assist radiologists in analyzing and interpreting medical images. Using a qualitative design, interviews were conducted with 7 radiologists (physicians), 4 radiologic technologists, and 1 physician assistant. Their experience within radiology varied between 13 months and 38 years. The data were analyzed using qualitative content analysis.ResultsParticipants cited numerous strengths and advantages of significant value in integrating AI into radiology practice. Numerous challenges were also revealed in terms of difficulties associated with choosing, acquiring, and deploying the AI application and operational issues in radiology practice. They discussed experiences with diverse strategies to facilitate the integration of AI in radiology and to address various challenges and problems.ConclusionsThe findings illustrate how AI integration was experienced in 1 hospital. While not generalizable, the study provides insights that may be useful for similar settings. Radiology staff believed AI integration enhanced decision-making and quality of care, but they encountered challenges from preadoption to routine use of AI in radiology practice. Strategies such as internal training and workflow adaptation facilitated the successful integration of AI in radiology.

Educational Competencies for Artificial Intelligence in Radiology: A Scoping Review.

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

AI and ML in radiology: Making progress

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.

Impact of the rise of artificial intelligence in radiology: What do radiologists think?