Medical students' perception of AI's role in radiology before and after an AI-focused educational panel: a paired pre-post design.

BackgroundThe growing use of artificial intelligence (AI) in healthcare necessitates understanding the perspectives of future practitioners. This study investigated the perceptions of German-speaking medical and dental students regarding the role of artificial intelligence (AI) in their future practices.MethodsA 28-item survey adapted from the AI in Healthcare Education Questionnaire (AIHEQ) and the Medical Student’s Attitude Toward AI in Medicine (MSATAIM) scale was administered to students in Austria, Germany, and Switzerland from April to July 2023. Participants were recruited through targeted advertisements on Facebook and Instagram and were required to be proficient in German and enrolled in medical or dental programs. The data analysis included descriptive statistics, correlations, t tests, and thematic analysis of the open-ended responses.ResultsOf the 409 valid responses (mean age = 23.13 years), only 18.2% of the participants reported receiving formal training in AI. Significant positive correlations were found between self-reported tech-savviness and AI familiarity (r = 0.67) and between confidence in finding reliable AI information and positive attitudes toward AI (r = 0.72). While no significant difference in AI familiarity was found between medical and dental students, dental students exhibited slightly more positive attitudes toward the integration of AI into their future practices.ConclusionThis study underscores the need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice.

To evaluate US radiologists' attitudes towards artificial intelligence (AI) in radiology, identify demographic factors influencing these perceptions, and analyse the potential challenges and opportunities AI integration presents in radiological practice. A cross-sectional survey of 322 board-certified radiologists was conducted using Amazon Mechanical Turk (MTurk) and Qualtrics. The survey collected demographic data (age, gender, experience, and subspecialty) and assessed attitudes towards AI integration in radiology. Pearson's chi-square tests were used to evaluate correlations between demographic variables and perceptions of AI's impact, confidence in its role, and anticipated adoption timelines. The majority of radiologists (82.9%) indicated that AI would significantly impact radiology. Younger radiologists (<40 years) displayed higher optimism and greater familiarity with AI tools compared to their older counterparts. Statistical analysis revealed significant correlations between age and optimism (χ2 = 47.551, P < .001) and between gender and confidence in AI's role (χ2 = 21.982, P < .001). Subspecialty differences emerged, with 87.5% of emergency radiologists anticipating AI adoption within 3-5 years, whereas 26.3% of paediatric radiologists predicted adoption within 6-10 years. Notably, younger radiologists showed increased susceptibility to errors when evaluating misleading AI-generated outputs, underscoring the necessity for structured training programs. The integration of AI in radiology holds transformative potential but poses challenges, including overreliance, varying familiarity levels, and subspecialty-specific disparities. Structured education and robust regulatory frameworks are critical to optimize AI's adoption and minimize associated risks. This study highlights significant demographic variations in radiologists' attitudes towards AI and underscores the importance of targeted training and interventions to support effective AI integration. These findings add to the existing research by emphasizing the necessity for structured AI training tailored to demographic and subspecialty needs.

Background: Artificial Intelligence (AI) is revolutionizing radiology practice by supporting diagnostic accuracy and efficiency, yet many radiologists have not integrated it into their daily workflows. However, literature on radiology AI knowledge and perceptions among medical students is limited, both globally and within Saudi Arabia. Objectives: This study evaluates the extent of knowledge and understanding of AI applications in radiology among medical students from various universities in Saudi Arabia. Methodology: We conducted a cross-sectional study involving 351 medical students from different universities in Saudi Arabia. The online questionnaire included demographic characteristics and questions regarding awareness of AI applications in radiology, as well as preferred learning methods. Descriptive statistics were used for basic understanding, and the chi-square test was employed to assess associations between demographic characteristics and awareness of AI. Results: The survey revealed that 66.9% of participants had some level of awareness regarding AI in radiology, whereas 33.1% had little or no knowledge of it. The presence of a dedicated radiology module in the curriculum was significantly associated with higher awareness of AI applications (p &amp;lt; 0.001). The preferred learning methods included lectures (47.3%), workshops (43.0%), and extracurricular activities (35.3%). A total of 59.3% of students agreed or strongly agreed that AI should be included in the medical curriculum; however, only a quarter felt they had adequate opportunities to learn about it. Conclusion: There is a high level of awareness of AI among medical students in Saudi Arabia, but this does not equate to comprehensive education on all aspects of AI use in radiology. The results highlight a lack of formalized AI training in medical schools, suggesting the need for its integration into the curriculum to prepare future physicians who will increasingly rely on this technology in their practice. A collaborative focus on both the technical and ethical aspects of AI is necessary as we continue to explore this issue.

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

The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.

BackgroundThe integration of artificial intelligence (AI) in radiology has the potential to improve diagnostic accuracy and efficiency. Medical students and junior doctors will likely use AI more frequently in the future, making their perceptions essential for identifying educational gaps.PurposeTo explore the perceptions of UK medical students and junior doctors regarding AI in radiology.Material and MethodsA cross-sectional survey was distributed across UK medical schools and foundation programs. A total of 250 responses were analyzed using descriptive statistics and non-parametric tests, focusing on career impact, clinical effectiveness, educational development, and ethical concerns.ResultsMost respondents (55.2%) were undeterred by career uncertainties related to AI, with 64% confident that AI would not replace radiologists. Up to 80.6% supported AI's clinical benefits, and 63.2% endorsed its educational integration. However, there were concerns about job displacement and insufficient AI training. Medical students were more worried about job security than junior doctors, while those committed to radiology were less apprehensive and viewed AI as complementary.ConclusionEducational programs and regulatory frameworks are essential to facilitate AI integration in radiology. Addressing concerns about job displacement and improving AI education will be key to preparing future radiologists for technological advancements.

e13657 Background: Artificial intelligence (AI) is rapidly transforming medical education and how healthcare services are delivered. Oncology healthcare professionals (HCPs) can significantly benefit from the advances of AI including optimized diagnostic capabilities, treatment plans, resource allocation, patient outcomes and much more. The synergy between technology and healthcare is strengthening, heralding opportunities for a revolution in medical education and the broader healthcare industry. We developed an introductory AI workshop and conducted pre- and post-surveys, aiming to inform the design of successive workshops and an AI curriculum, to access knowledge, attitudes and perceived challenges for AI integration into Medical Oncology. Methods: An interactive one-hour workshop was conducted for physicians and faculty of the Hematology and Oncology department. 18 participants, encompassing 6 physicians and 12 other HCPs completed a pre- and post-workshop survey. We evaluated participants' knowledge, opinions, and perceived limitations regarding AI in oncology research, practitioner education, and clinical applications using both open and closed-ended questions, such as multiple-choice and 1-5 Likert scale formats. Results: Survey results included 18 pre-workshop and 17 post-workshop participants, 83% had no formal computer science training: AI knowledge in medicine and awareness of clinical AI applications increased by 100%. Interestingly 24% felt the workshop influenced their view on AI's role in their medical career and better prepared for AI-related challenges. 58% believed AI would assist clinicians within 5 years. However, the belief that AI will play a significant role in their own career did not change after the intervention. Data privacy concerns increased by 25%, while concerns about physician over-reliance on AI decreased by 15%. Notably, 76% were motivated to engage with AI during their future practice after the workshop. Conclusions: The AI initiative significantly enhanced oncology healthcare professionals' understanding and readiness for AI integration, despite most lacking prior formal AI education. A marked increase in AI knowledge and motivation to use AI in practice was noted, although perceptions of AI's impact in their day-to-day work remained unchanged with one workshop, contrasting expert views and predictions. Data privacy concerns rose, but decreased fears of over-reliance on AI indicate a more balanced perspective. These findings stress the need for ongoing AI education in healthcare and underscore the importance of baseline evaluations and feedback for shaping future workshops. Since, AI's integration into society is poised to transform healthcare, an AI curriculum is essential.

The integration of artificial intelligence (AI) in healthcare necessitates AI literacy within medical education.As AI's role in health care expands, understanding algorithm transparency, accountability and bias is crucial.However, incorporating AI education into an already dense curriculum poses challenges.A structured, efficient course covering both technical and ethical aspects of AI is essential to prepare future clinicians for AI-enabled health care. | WHAT WAS TRIED?We developed a one-credit, 18-hour AI literacy course for medical students, balancing theoretical foundations with experiential learning.The course structure comprised a 3-hour lecture on fundamental AI concepts, two 6-hour hands-on workshops where students worked in groups of three to four and a concluding 3-hour discussion and reflection session.These sessions were strategically designed to ensure engagement while accommodating students' demanding schedules.Shorter, more frequent sessions were considered but deemed impractical due to scheduling constraints and the challenge of effectively conducting hands-on activities in a fragmented format.The course was initially introduced in 2020 and 2021 for secondyear medical students, attracting 11 and 13 students, respectively (23% of the cohort).Based on student feedback, it was revised in 2022 to target senior students (fifth-and sixth-year), increasing participation to 33%.In the workshops, students developed and deployed AI models (e.g., knee fracture detection, wound segmentation), guided by a data scientist and a clinician with expertise in the AI topic, fostering interdisciplinary collaboration.Key topics like privacy, bias, data security and patient autonomy were integrated into projects, prompting reflection on social impacts such as ethical AI use and healthcare disparities.Project themes were selected based on faculty expertise and contemporary AI applications, ensuring clinical relevance.Student learning was assessed using a 17-competency framework, 1 measuring AI literacy before and after the course to evaluate effectiveness and inform future improvements.3 | WHAT LESSONS WERE LEARNED?Transitioning the course to senior medical students enhanced engagement and comprehension, aligning AI concepts with clinical applications.Quantitative assessments showed substantial improvements in AI literacy, particularly in 'AI's strengths and weaknesses' (RS 1.6), 'data literacy' (RS 1.3), 'critically interpreting data' (RS 1.15) and 'ethics' (RS 1.15).Constructive feedback from students, collected via structured surveys, highlighted the value of hands-on experience, interdisciplinary learning and real-world AI applications.The design and implementation of our 18-hour AI literacy course provide insights into integrating AI education within medical training.First, while AI education programmes vary in length-from brief workshops to full-semester courses-our approach demonstrates that an intensive yet feasible structure enables medical students to develop key competencies within a compact timeframe.Second, curricula should emphasize hands-on learning, guiding students through real-world AI challenges and ethical considerations.Third, AI literacy training may best target senior medical students with more clinical experience, preparing them to use AI independently.Lastly, challenges included diverse student technical backgrounds and the rapid evolution of AI, requiring continuous faculty upskilling.These challenges highlight the need for adaptive AI curricula that evolve with technological advancements and learner needs.

Teaching Artificial Intelligence Literacy: A Challenge in the Education of Radiology Residents

The integration of Artificial Intelligence (AI) in higher education has gained significant momentum, particularly in enhancing teaching methodologies and student learning outcomes. In higher education, AI enhances personalized learning, boosts student motivation, and improves academic achievement. In the context of Arts and Science colleges in Erode, there is a growing interest in adopting AI tools such as chatbots, virtual labs, and personalized learning platforms. However, the actual impact of these technologies on students’ academic performance and learning experiences remains underexplored. The integration of Artificial Intelligence in education faces several challenges in Arts and Science colleges in Erode. One major issue is the lack of awareness among students about the available AI tools and their potential academic benefits. Additionally, unequal access to AI resources across colleges hampers consistent usage. Students also exhibit varying levels of engagement with AI tools, which may affect their academic outcomes. Hence, the researchers developed a study for exploring the role of artificial intelligence in enhancing educational outcomes of college students in Erode, Tamilnadu. A descriptive research design is employed to align with the study’s objectives. Both primary and secondary data sources are used to gather information. The study targets students from Arts and Science colleges in Erode, Tamil Nadu, with a sample size of 185 selected through random sampling. The necessary data is collected using a structured questionnaire that covers students’ demographic profiles, AI tool usage, and their perceptions of AI’s role in enhancing educational outcomes, assessed through a 5-point Likert scale. The data are organized using MS Excel and analyzed using statistical techniques such as percentage analysis, mean scores, standard deviation, and ANOVA via SPSS 26.0 software. Additionally, null hypotheses are formulated to examine significant differences in the perceived role of AI across selected independent variables. This study mentioned from analysis that maximum level of experience with AI’s role in enhancing educational outcomes is perceived by the students belong to male, Science stream, studying second-year, using Virtual Labs (AI tool) and using AI tools frequently.

Introduction: Applications of artificial intelligence (AI) in radiology continue to increase every year, however most radiology residencies lack a dedicated AI education curriculum. Fundamental AI education resources are even more sparse for trainees in low- to middle-income countries and under-resourced healthcare systems. The AI Literacy Course assesses the effectiveness and scalability of a free, remote AI education curriculum to increase understanding of fundamental AI terms, methods, and applications in radiology among radiology trainees in the United States and internationally. Method: A week-long AI in radiology literacy course for radiology trainees was held October 3-7, 2022. Ten 30-minute lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by three different subspecialties, and special topics lectures. A proctored, hands-on clinical AI session allowed participants to directly use an FDA-cleared, AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants&amp;rsquo; knowledge of AI terminology and applications, as well as their interest in AI education. Results: A total of 25 residency programs throughout the US participated in the course with participants attending from 10 countries. An average of 150 participants viewed the course per day. Nearly all participants reported insufficient exposure to AI in their radiology training (95.8%). Participant knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course evaluation of 8.3/15 and a post-course evaluation of 10.0/15 (p=0.01). Conclusion: The scalability of the AI Literacy Course demonstrates a viable model to bring accessible fundamental AI education to radiology trainees in the United States and internationally.

The transition of healthcare systems towards digitalization, particularly through the integration of artificial intelligence (AI), is reshaping medical practice and education. AI's role in enhancing diagnosis, patient care, and distance education is becoming increasingly significant, prompting a need for strategic planning, investment, and training in the healthcare workforce. This study focuses on the attitudes of health science students at the University of Fujairah towards AI in medical services, particularly in developing countries where AI can address personnel shortages. A literature review reveals that while health science students globally exhibit positive attitudes towards AI, gaps in knowledge and skills persist, necessitating improved educational programs. The study employs a quantitative methodology, utilizing a standardized questionnaire to assess students' perceptions of AI's impact on healthcare efficiency, patient engagement, and ethical concerns. The sample comprises 92 students, ensuring representation across various academic disciplines. Findings indicate a duality in students' perspectives: while there is enthusiasm for AI's transformative potential, concerns about data privacy and the erosion of personal interactions in patient care are prevalent. Gender differences emerge, with male students showing higher trust in AI, while female students express greater apprehension regarding data security. As students progress in their studies, they become more critical of AI's impact on personal interactions, highlighting the need for educational programs to address these concerns. In conclusion, the study underscores the importance of integrating AI education into healthcare curricula, focusing on data privacy and patient-centered approaches. Recommendations include enhancing early-year educational modules on AI and conducting further research to understand the evolving perceptions of students towards AI in healthcare. This research provides a foundation for developing strategies that ensure the effective integration of AI while maintaining the essential human touch in patient care.

Objectives The purpose of this study is to empirically verify the relationship between technology utilization ability and awareness of artificial intelligence and coding education for pre-service early childhood teachers. Methods For this purpose, a survey was conducted on 180 pre-service early childhood teachers majoring in child studies and early childhood education at a university located in J city, Gyeongsangnam-do, and the collected data were analyzed using SPSS program to verify reliability, mean comparison, and correlation analysis. Results As for the difference in perception of research variables according to the demographic characteristics of pre-service early childhood teachers, pre-service early childhood teachers who completed information literacy education and had experience in artificial intelligence and coding education showed high level of perception of technology utilization ability and artificial intelligence and coding education. In addition, there was a significant positive correlation between technology utilization ability and the perception of artificial intelligence and coding education. Conclusions In the era of the Fourth Industrial Revolution, early childhood teachers should cultivate information- related skills. Therefore, educational institutions that train early childhood teachers should actively review ways to strengthen education related to technology utilization education, artificial intelligence education, and coding education.

ObjectivesCurrently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs.MethodsBetween April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression.ResultsThe survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%).ConclusionsBroad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation.Key Points• There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty.• Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles.• Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

Artificial intelligence (AI) has alarmed the society of Taiwan believing it is responsible for potential surveillance, data theft and abuse, and other privacy infringements. By adopting the theory of motivated reasoning, this study explores how Taiwanese people’s perceptions of AI are affected by their institutional trust, attitudes toward the government and corporations, which are the two most common sponsors of scientific development. First, findings establish that respondents’ science trust in AI is made up of perceptions of AI and its science community, and they have lower faith in the AI science community than in AI alone. Second, the perceptions of both AI and its science community are positively associated with trust in government and corporations. Third, scientific news has a direct bearing on AI trust, but not on either government or corporation trust. By contrast, political news has no effect on either trust in AI or its science community, yet trust in government and corporations mediates the influence of political news on trust in AI and its science community. Finally, demographic variables hardly predict trust in AI, AI science community, government, and corporations, but education and gender are directly related to news consumption, which further influences institutional and science trust.