Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career?

Impact of artificial intelligence on the academic performance and test anxiety of pharmacy students in objective structured clinical examination: a randomized controlled trial

BACKGROUND

The rapid advancement of generative artificial intelligence (AI) in recent years has led to its increased application across various fields including education. One area where AI can significantly impact is clinical education, particularly in the preparation and execution of objective structured clinical examinations (OSCEs).

AIM

This study aimed to evaluate the impact of AI-generated study material and feedback on the academic performance and level of anxiety of pharmacy students in formative OSCE.

METHOD

This was a 4-week (June-July 2024) randomized controlled study. Students of 6th semester PharmD program were randomized into either an intervention or control group. The intervention group received intervention which comprised a comprehensive training session on how to use AI tools (ChatGPT, Gemini and Perplexity) for generating study materials with personalized feedback, in addition to usual OSCE instructions. The control group only received the usual OSCE instructions. In addition, all students completed the test anxiety inventory (TAI) questionnaire before the OSCE.

RESULTS

Eighty-eight (40 male, 48 female) out of 92 (96%) students attended the OSCE and completed the TAI questionnaire. Each group had 44 (50%) students. The mean OSCE mark was 13.26 (± 5.05) out of 30. No significant difference was found between the intervention [12.98 (± 5.15)] and control [13.54 (± 5.00)] groups regarding mean OSCE marks (p = 0.550). Similarly, no significant difference was found between the groups regarding the total TAI score (p = 0.917).

CONCLUSION

While the use of AI tools did not improve the academic performance of students or reduce test-related anxiety, they did not negatively impact these outcomes either. Future research should investigate the long-term effects of AI-based interventions on educational outcomes.

Encompassing trust in medical AI from the perspective of medical students: a quantitative comparative study

BACKGROUND

In the years to come, artificial intelligence will become an indispensable tool in medical practice. The digital transformation will undoubtedly affect today's medical students. This study focuses on trust from the perspective of three groups of medical students - students from Croatia, students from Slovakia, and international students studying in Slovakia.

METHODS

A paper-pen survey was conducted using a non-probabilistic convenience sample. In the second half of 2022, 1715 students were surveyed at five faculties in Croatia and three in Slovakia.

RESULTS

Specifically, 38.2% of students indicated familiarity with the concept of AI, while 44.8% believed they would use AI in the future. Patient readiness for the implementation of technologies was mostly assessed as being low. More than half of the students, 59.1%, believe that the implementation of digital technology (AI) will negatively impact the patient-physician relationship and 51,3% of students believe that patients will trust physicians less. The least agreement with the statement was observed among international students, while a higher agreement was expressed by Slovak and Croatian students 40.9% of Croatian students believe that users do not trust the healthcare system, 56.9% of Slovak students agree with this view, while only 17.3% of international students share this opinion. The ability to explain to patients how AI works if they were asked was statistically significantly different for the different student groups, international students expressed the lowest agreement, while the Slovak and Croatian students showed a higher agreement.

CONCLUSION

This study provides insight into medical students' attitudes from Croatia, Slovakia, and international students regarding the role of artificial intelligence (AI) in the future healthcare system, with a particular emphasis on the concept of trust. A notable difference was observed between the three groups of students, with international students differing from their Croatian and Slovak colleagues. This study also highlights the importance of integrating AI topics into the medical curriculum, taking into account national social & cultural specificities that could negatively impact AI implementation if not carefully addressed.

Bridging the Artificial Intelligence (AI) Divide: Do Postgraduate Medical Students Outshine Undergraduate Medical Students in AI Readiness?

INTRODUCTION

As artificial intelligence (AI) transforms healthcare, medical education must adapt to equip future physicians with the necessary competencies. However, little is known about the differences in AI knowledge, attitudes, and practices between undergraduate and postgraduate medical students. This study aims to assess and compare AI knowledge, attitudes, and practices among undergraduate and postgraduate medical students, and to explore the associated factors and qualitative themes.

METHODS

A mixed-methods study was conducted, involving 605 medical students (404 undergraduates, 201 postgraduates) from a tertiary care center. Participants completed a survey assessing AI knowledge, attitudes, and practices. Semi-structured interviews and focus group discussions were conducted to explore qualitative themes. Quantitative data were analyzed using descriptive statistics, t-tests, chi-square tests, and regression analyses. Qualitative data underwent thematic analysis.

RESULTS

Postgraduate students demonstrated significantly higher AI knowledge scores than undergraduates (38.9±4.9 vs. 29.6±6.8, p<0.001). Both groups held positive attitudes, but postgraduates showed greater confidence in AI's potential (p<0.001). Postgraduates reported more extensive AI-related practices (p<0.001). Key qualitative themes included excitement about AI's potential, concerns about job security, and the need for AI education. AI knowledge, attitudes, and practices were positively correlated (p<0.01).

CONCLUSIONS

This study reveals a significant AI knowledge gap between undergraduate and postgraduate medical students, highlighting the need for targeted AI education. The findings can inform curriculum development and policies to prepare medical students for the AI-driven future of healthcare. Further research should explore the long-term impact of AI education on clinical practice.

Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology

Study Purpose

This study aims to analyze radiologic technology student's perceptions of artificial intelligence (AI) and its applications in radiology.

Methods

A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students.

Results

Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI.

Conclusion

The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.

Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study

OBJECTIVES

We aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.

METHODS

A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses.

RESULTS

Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies.

CONCLUSIONS

Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum.

CLINICAL RELEVANCE STATEMENT

This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula.

KEY POINTS

• Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology. • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea. • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.

Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks

BACKGROUND OF STUDY

Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan.

MATERIAL AND METHODS

A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression.

RESULTS

Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187).

CONCLUSION

Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.

A scoping review of artificial intelligence in medical education: BEME Guide No. 84

BACKGROUND

Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research.

METHODS

This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape.

RESULTS

The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.

CONCLUSION

The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

International pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study

AIMS

To explore international undergraduate pharmacy students' views on integrating artificial intelligence (AI) into pharmacy education and practice.

METHODS

This cross-sectional institutional review board-approved multinational, multicentre study comprised an anonymous online survey of 14 multiple-choice items to assess pharmacy students' preferences for AI events in the pharmacy curriculum, the current state of AI education, and students' AI knowledge and attitudes towards using AI in the pharmacy profession, supplemented by 8 demographic queries. Subgroup analyses were performed considering sex, study year, tech-savviness, and prior AI knowledge and AI events in the curriculum using the Mann-Whitney U-test. Variances were reported for responses in Likert scale format.

RESULTS

The survey gathered 387 pharmacy student opinions across 16 faculties and 12 countries. Students showed predominantly positive attitudes towards AI in medicine (58%, n = 225) and expressed a strong desire for more AI education (72%, n = 276). However, they reported limited general knowledge of AI (63%, n = 242) and felt inadequately prepared to use AI in their future careers (51%, n = 197). Male students showed more positive attitudes towards increasing efficiency through AI (P = .011), while tech-savvy and advanced-year students expressed heightened concerns about potential legal and ethical issues related to AI (P < .001/P = .025, respectively). Students who had AI courses as part of their studies reported better AI knowledge (P < .001) and felt more prepared to apply it professionally (P < .001).

CONCLUSIONS

Our findings underline the generally positive attitude of international pharmacy students towards AI application in medicine and highlight the necessity for a greater emphasis on AI education within pharmacy curricula.

The Impact of Artificial Intelligence on the Preference of Radiology as a Future Specialty Among Medical Students at Jazan University, Saudi Arabia: A Cross-Sectional Study

Background The use of artificial intelligence (AI) in healthcare continues to spark interest and has been the subject of extensive discussion in recent years as well as its potential effects on future medical specialties, including radiology. In this study, we aimed to study the impact of AI on the preference of medical students at Jazan University in choosing radiology as a future specialty. Methodology An observational cross-sectional study was conducted using a pre-tested self-administered online questionnaire among medical students at Jazan University. Data were cleaned, coded, entered, and analyzed using SPSS (SPSS Inc., USA) version 25. Statistical significance was defined as a P-value of less than 0.05. We examined the respondents' preference for radiology rankings with the presence and absence of AI. Radiology's ranking as a preferred specialty with or without AI integration was statistically analyzed for associations with baseline characteristics, personal opinions, and previous exposures among those who had radiology as one of their top three options. Results Approximately 27.4% of males and 28.3% of females ranked radiology among their top three preferred choices. Almost 65.2% were exposed to radiology topics through pre-clinical lectures. The main sources of information about AI for the studied group were medical students (41%) and the Internet (27.5%). The preference of students for radiology was significantly affected when it is assessed by AI (P < 0.05). Around (16.1%) of those who chose radiology as one of their top three choices strongly agree that AI will decrease the job opportunities for radiologists. Logistic regression analysis showed that being a female is significantly associated with an increased chance to replace radiology with other specialty when it is integrated with AI (Crude odds ratio (COR) = 1.91). Conclusion Our results demonstrated that the students' choices were significantly affected by the presence of AI. Thereover, to raise medical students' knowledge and awareness of the potential positive effects of AI, it is necessary to organize an educational campaign, webinars, and conferences.

Assessing the Level of Understanding (Knowledge) and Awareness of Diagnostic Imaging Students in Ghana on Artificial Intelligence and Its Applications in Medical Imaging

Introduction

Recent advancements in technology have propelled the applications of artificial intelligence (AI) in various sectors, including healthcare. Medical imaging has benefited from AI by reducing radiation risks through algorithms used in examinations, referral protocols, and scan justification. This research work assessed the level of knowledge and awareness of 225 second- to fourth-year medical imaging students from public universities in Ghana about AI and its prospects in medical imaging.

Methods

This was a cross-sectional quantitative study design that used a closed-ended questionnaire with dichotomous questions, designed on Google Forms, and distributed to students through their various class WhatsApp platforms. Responses were entered into an Excel spreadsheet and analyzed with the Statistical Package for the Social Sciences (SPSS) software version 25.0 and Microsoft Excel 2016 version.

Results

The response rate was 80.44% (181/225), out of which 97 (53.6%) were male, 82 (45.3%) were female, and 2 (1.1%) preferred not to disclose their gender. Among these, 133 (73.5%) knew that AI had been incorporated into current imaging modalities, and 143 (79.0%) were aware of AI's emergence in medical imaging. However, only 97 (53.6%) were aware of the gradual emergence of AI in the radiography industry in Ghana. Furthermore, 160 people (88.4%) expressed an interest in learning more about AI and its applications in medical imaging. Less than one-third (32%) knew about the general basic application of AI in patient positioning and protocol selection. And nearly two-thirds (65%) either felt threatened or unsure about their job security due to the incorporation of AI technology in medical imaging equipment. Less than half (38% and 43%) of the participants acknowledged that current clinical internships helped them appreciate the role of AI in medical imaging or increase their level of knowledge in AI, respectively. Discussion. Generally, the findings indicate that medical imaging students have fair knowledge about AI and its prospects in medical imaging but lack in-depth knowledge. However, they lacked the requisite awareness of AI's emergence in radiography practice in Ghana. They also showed a lack of knowledge of some general basic applications of AI in modern imaging equipment. Additionally, they showed some level of misconception about the role AI plays in the job of the radiographer.

Conclusion

Decision-makers should implement educational policies that integrate AI education into the current medical imaging curriculum to prepare students for the future. Students should also be practically exposed to the various incorporations of AI technology in current medical imaging equipment.

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations

PURPOSE

There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members.

METHODS

An anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized.

RESULTS

A total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years' experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%).

CONCLUSION

ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Changes in Radiology Due to Artificial Intelligence That Can Attract Medical Students to the Specialty

The role of artificial intelligence (AI) in radiology has grown exponentially in the recent years. One of the primary worries by medical students is that AI will cause the roles of a radiologist to become automated and thus obsolete. Therefore, there is a greater hesitancy by medical students to choose radiology as a specialty. However, it is in this time of change that the specialty needs new thinkers and leaders. In this succinct viewpoint, 2 medical students involved in AI and 2 radiologists specializing in AI or clinical informatics posit that not only are these fears false, but the field of radiology will be transformed in such a way due to AI that there will be novel reasons to choose radiology. These new factors include greater impact on patient care, new space for innovation, interdisciplinary collaboration, increased patient contact, becoming master diagnosticians, and greater opportunity for global health initiatives, among others. Finally, since medical students view mentorship as a critical resource when deciding their career path, medical educators must also be cognizant of these changes and not give much credence to the prevalent fearmongering. As the field and practice of radiology continue to undergo significant change due to AI, it is urgent and necessary for the conversation to expand from expert to expert to expert to student. Medical students should be encouraged to choose radiology specifically because of the changes brought on by AI rather than being deterred by it.

Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?

The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.

Artificial Intelligence and Radiology Education

Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.

Malaysian Medical Students' Attitudes and Readiness Toward AI (Artificial Intelligence): A Cross-Sectional Study

OBJECTIVES

The Malaysian health ministry has started introducing artificial intelligence (AI) technology to aid local healthcare delivery. This study aims to survey Malaysian medical students' attitudes toward AI and evaluate their readiness to work with medical AI technology.

METHODS

An online questionnaire on Google Forms was distributed to all 31 medical schools in Malaysia. The questionnaire consists of 3 sections: the first part surveyed the participants' demographics, the second assessed the participants' attitudes toward AI, and the final part utilizes the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) scale to evaluate their AI readiness.

RESULTS

Three hundred and one students from 17 universities in Malaysia responded to the questionnaire. 87.36% of students agreed that AI will play an essential role in healthcare; 32.55% of students were less likely to consider a career in radiology due to the advancement of AI. The majority of students (71%) felt that teaching in AI will benefit their careers, while 69.44% agreed that all students should receive teaching in AI. Around 44.5% of students felt that they will possess the knowledge required to work with AI upon graduation. On the MAIRS-MS scale, students had a mean score of 21 of 40 for the cognitive factor, 25 of 40 for the ability factor, 10 of 15 for the vision factor, and 11 of 15 for the ethics factor. Overall, Malaysian students had a mean total score of 67±14.8 out of 110.

CONCLUSION

Malaysian medical students have demonstrated awareness of AI and a willingness to learn more about it. More work needs to be done to improve students' AI readiness, particularly their knowledge and application of AI technology. Malaysian universities should start to work on incorporating AI teaching into their curricula.

Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace

Biometric technologies are becoming more pervasive in the workplace, augmenting managerial processes such as hiring, monitoring and terminating employees. Until recently, these devices consisted mainly of GPS tools that track location, software that scrutinizes browser activity and keyboard strokes, and heat/motion sensors that monitor workstation presence. Today, however, a new generation of biometric devices has emerged that can sense, read, monitor and evaluate the affective state of a worker. More popularly known by its commercial moniker, Emotional AI, the technology stems from advancements in affective computing. But whereas previous generations of biometric monitoring targeted the exterior physical body of the worker, concurrent with the writings of Foucault and Hardt, we argue that emotion-recognition tools signal a far more invasive disciplinary gaze that exposes and makes vulnerable the inner regions of the worker-self. Our paper explores attitudes towards empathic surveillance by analyzing a survey of 1015 responses of future job-seekers from 48 countries with Bayesian statistics. Our findings reveal affect tools, left unregulated in the workplace, may lead to heightened stress and anxiety among disadvantaged ethnicities, gender and income class. We also discuss a stark cross-cultural discrepancy whereby East Asians, compared to Western subjects, are more likely to profess a trusting attitude toward EAI-enabled automated management. While this emerging technology is driven by neoliberal incentives to optimize the worksite and increase productivity, ultimately, empathic surveillance may create more problems in terms of algorithmic bias, opaque decisionism, and the erosion of employment relations. Thus, this paper nuances and extends emerging literature on emotion-sensing technologies in the workplace, particularly through its highly original cross-cultural study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00146-021-01290-1.

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

Background

Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance.

Materials and methods

We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world.

Results

Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes.

Conclusion

Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

An evaluation of information online on artificial intelligence in medical imaging

Background

Opinions seem somewhat divided when considering the effect of artificial intelligence (AI) on medical imaging. The aim of this study was to characterise viewpoints presented online relating to the impact of AI on the field of radiology and to assess who is engaging in this discourse.

Methods

Two search methods were used to identify online information relating to AI and radiology. Firstly, 34 terms were searched using Google and the first two pages of results for each term were evaluated. Secondly, a Rich Search Site (RSS) feed evaluated incidental information over 3 weeks. Webpages were evaluated and categorized as having a positive, negative, balanced, or neutral viewpoint based on study criteria.

Results

Of the 680 webpages identified using the Google search engine, 248 were deemed relevant and accessible. 43.2% had a positive viewpoint, 38.3% a balanced viewpoint, 15.3% a neutral viewpoint, and 3.2% a negative viewpoint. Peer-reviewed journals represented the most common webpage source (48%), followed by media (29%), commercial sources (12%), and educational sources (8%). Commercial webpages had the highest proportion of positive viewpoints (66%). Radiologists were identified as the most common author group (38.9%). The RSS feed identified 177 posts of which were relevant and accessible. 86% of posts were of media origin expressing positive viewpoints (64%).

Conclusion

The overall opinion of the impact of AI on radiology presented online is a positive one. Consistency across a range of sources and author groups exists. Radiologists were significant contributors to this online discussion and the results may impact future recruitment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01209-4.

Medical Students' Perceptions towards Digitization and Artificial Intelligence: A Mixed-Methods Study

Digital technologies in health care, including artificial intelligence (AI) and robotics, constantly increase. The aim of this study was to explore attitudes of 2020 medical students’ generation towards various aspects of eHealth technologies with the focus on AI using an exploratory sequential mixed-method analysis. Data from semi-structured interviews with 28 students from five medical faculties were used to construct an online survey send to about 80,000 medical students in Germany. Most students expressed positive attitudes towards digital applications in medicine. Students with a problem-based curriculum (PBC) in contrast to those with a science-based curriculum (SBC) and male undergraduate students think that AI solutions result in better diagnosis than those from physicians (p < 0.001). Male undergraduate students had the most positive view of AI (p < 0.002). Around 38% of the students felt ill-prepared and could not answer AI-related questions because digitization in medicine and AI are not a formal part of the medical curriculum. AI rating regarding the usefulness in diagnostics differed significantly between groups. Higher emphasis in medical curriculum of digital solutions in patient care is postulated.

Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

PURPOSE

Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel's familiarity with AI applications and its usefulness in clinical practice were investigated.

METHODS

A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants' knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants' trust level was also measured; Kruskal-Wallis test was used to examine differences between groups.

RESULTS

A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038).

CONCLUSION

Radiology personnel's knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.