Investigating Students' Perceptions towards Artificial Intelligence in Medical Education

Knowledge and use, perceptions of benefits and limitations of artificial intelligence chatbots among Italian physiotherapy students: a cross-sectional national study

BACKGROUND

Artificial Intelligence (AI) Chatbots (e.g., ChatGPT, Microsoft Bing, and Google Bard) can emulate human interaction and may support physiotherapy education. Despite growing interest, physiotherapy students' perspectives remain unexplored. This study investigated Italian physiotherapy students' knowledge, use, and perception of the benefits and limitations of AI Chatbots.

METHODS

A cross-sectional study was conducted through Survey Monkey from February to June 2024. One thousand five hundred and thirty-one physiotherapy students from 10 universities were involved. The survey consisted of 23 questions investigating: (a) respondent characteristics, (b) AI Chatbot knowledge and use, (c) perceived benefits, and (d) limitations. Multiple-choice and Likert-scale-based questions were adopted. Factors associated with knowledge, use, and perceptions of AI were explored using logistic regression models.

RESULTS

Of 589 students (38%) that completed the survey, most were male (n = 317; 53.8%) with a mean age of 22 years (SD = 3.88). Nearly all (n = 561; 95.3%) had heard of AI Chatbots, but 53.7% (n = 316) never used these tools for academic purposes. Among users, learning support was the most common purpose (n = 187; 31.8%), while only 9.9% (n = 58) declared Chatbot use during internships. Students agreed that Chatbots have limitations in performing complex tasks and may generate inaccurate results (median = 3 out of 4). However, they neither agreed nor disagreed about Chatbots' impact on academic performance, emotional intelligence, bias, and fairness (median = 2 out of 4). The students agreed to identify the risk of misinformation as a primary barrier (median = 3 out of 4). In contrast, they neither agreed nor disagreed on content validity, plagiarism, privacy, and impacts on critical thinking and creativity (median = 2 out of 4). Young students had 11% more odds of being familiar with Chatbots than older students (OR = 0.89; 95%CI 0.84-0.95; p = < 0.01), whereas female students had 39% lesser odds than males to have used Chatbots for academic purposes (OR = 0.61; 95%CI 0.44-0.85; p = < 0.01).

CONCLUSIONS

While most students recognize the potential of AI Chatbots, they express caution about their use in academia. Targeted training for students and faculty, supported by institutional and national guidelines, could guarantee a responsible integration of these technologies into physiotherapy education.

TRIAL REGISTRATION

Not applicable.

Artificial intelligence in surgical medicine: a brief review

The application of artificial intelligence (AI) technology in the medical field, particularly in surgical operations, has evolved from science fiction to a crucial tool. With continuous advancements in computational power and algorithmic technology, AI is reshaping the surgical medicine landscape. From preoperative diagnosis and planning to intraoperative real-time navigation and assistance and postoperative rehabilitation and follow-up management, AI technology has significantly enhanced the precision and safety of surgical procedures. This paper systematically reviews the development and current applications of AI in surgery, focusing on specific case studies of AI in surgical procedures, diagnostic assistance, intraoperative navigation, and postoperative management, highlighting its significant contributions to improving surgical precision and safety. Despite the obvious advantages of AI in improving surgical success, reducing postoperative complications, and accelerating patient recovery, its use in surgery still faces numerous challenges, including its cost-effectiveness, dependency, data privacy and security, clinical integration, and physician training. This review summarizes the current applications of AI in surgical medicine, highlights its benefits and limitations, and discusses the challenges and future directions of integrating AI into surgical practice.

Improving Nursing Students' Learning Outcomes in Neonatal Resuscitation: A Quasi-Experimental Study Comparing AI-Assisted Care Plan Learning With Traditional Instruction

AIM

The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation.

METHODS

Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction. The control group underwent traditional training consisting of lectures and skill demonstrations, while the experimental group underwent 4 weeks of training utilising an AI-based care plan learning approach. Neonatal resuscitation knowledge tests and student information questionnaires were used for pre- and post-test assessments.

RESULTS

When compared to the control group, the AI-based care plan group demonstrated noticeably greater learning achievement in newborn resuscitation. While the two groups' pre-test results were comparable, the AI-based education group's post-test results were noticeably higher than those of the traditional education group. Furthermore, most of the students had favourable opinions on AI applications and acknowledged their advantages for the nursing field.

CONCLUSION

The study's conclusions highlight the benefits of incorporating AI technology into nursing education and highlight how it might improve student learning outcomes for vital competencies like newborn resuscitation.

Exploring Artificial Intelligence Readiness in Medical Students: Analysis of a Global Survey

Introduction

The impact of artificial intelligence (AI) in diverse fields, including medical education, has emerged as a pivotal topic as the integration of AI technologies is becoming increasingly prevalent. This research delved into the landscape of AI integration in academic settings aimed to evaluate the students' readiness for the evolving AI landscape in medical education.

Materials and Methods

Participants were recruited from the International Collaboration and Exchange Program (ICEP) in the fall of 2023. An online survey was conducted to collect data on demographics, the landscape of AI utilization in academic settings, and the perceived readiness levels related to AI from 223 participants. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was used.

Results

Results indicated that 41.82% of participants "agreed" or "strongly agreed" that AI education should be part of medical training. Overall levels of AI readiness exhibited a statistically significant positive correlation with the frequency of AI inclusion in the curriculum (r = 0.217, p = 0.009), the frequency of AI use for studying (r = 0.246, p = 0.003), and the agreement that AI education should be integrated into medical training (r = 0.594, p < 0.001).

Conclusions

This study offers valuable insights into the ongoing discussion on the role of AI in education, providing a foundation for educators to consider the integration of AI into their educational framework. The implementation of AI education could potentially enhance students' AI readiness, considering the multiple benefits this symbiosis can offer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40670-024-02190-x.

Assessing the disconnect between student interest and education in artificial intelligence in medicine in Saudi Arabia

BACKGROUND

Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical education has struggled to stay ahead of the developing technology. The question of whether medical education is fully preparing trainees to adapt to potential changes from AI technology in clinical practice remains unanswered, and the influence of AI on medical students' career preferences remains unclear. Understanding the gap between students' interest in and knowledge of AI may help inform the medical curriculum structure.

METHODS

A total of 354 medical students were surveyed to investigate their knowledge of, exposure to, and interest in the role of AI in health care. Students were questioned about the anticipated impact of AI on medical specialties and their career preferences.

RESULTS

Most students (65%) were interested in the role of AI in medicine, but only 23% had received formal education in AI based on reliable scientific resources. Despite their interest and willingness to learn, only 20.1% of students reported that their school offered resources enabling them to explore the use of AI in medicine. They relied mainly on informal information sources, including social media, and few students understood fundamental AI concepts or could cite clinically relevant AI research. Students who cited more scientific primary sources (rather than online media) exhibited significantly higher self-reported understanding of AI concepts in the context of medicine. Interestingly, students who had received more exposure to AI courses reported higher levels of skepticism regarding AI and were less eager to learn more about it. Radiology and pathology were perceived to be the fields most strongly affected by AI. Students reported that their overall choice of specialty was not impacted by AI.

CONCLUSION

Formal AI education seems inadequate despite students' enthusiasm concerning the application of such technology in clinical practice. Medical curricula should evolve to promote structured, evidence-based AI literacy to enable students to understand the potential applications of AI in health care.

Perceptions and future perspectives of medical students on the use of artificial intelligence based chatbots: an exploratory analysis

Background

Artificial Intelligence (AI) has made a strong entrance into different fields such as healthcare, but currently, medical degree curricula are not adapted to the changes that adopting these types of tools entitles. It is important to understand the future needs of students to provide the most comprehensive education possible.

Objective

The aim of this teaching improvement project is to describe the knowledge, attitudes, and perspectives of medical students regarding the application of AI and chatbots with patients, also considering their ethical perceptions.

Methods

Descriptive cross-sectional analysis in which the participants were students enrolled in the subject "Preventive Medicine, Public Health and Applied Statistics" during the second semester of the 2023/24 academic year, corresponding to the fifth year of the Degree in Medicine at the University of Barcelona. The students were invited to complete a specific questionnaire anonymously and voluntarily, which they could respond to using their mobile devices by scanning a QR code projected on the classroom screen, we used Microsoft Forms to perform the survey.

Results

Out of the 61 students enrolled in the subject, 34 (56%) attended the seminar, of whom 29 (85%) completed the questionnaire correctly. Of those completing the questionnaire, 20 (69%) had never used chatbots for medical information, 19 (66%) expressed a strong interest in the practical applications of AI in medicine, 14 (48%) indicated elevated concern about the ethical aspects, 17 (59%) acknowledged potential biases in these tools, and 17 (59%) expressed at least moderate confidence in chatbot-provided information. Notably, 24 (83%) agreed that acquiring AI-related knowledge will be essential to effectively perform their future professional roles.

Conclusion

Surveyed medical students demonstrated limited exposure to AI-based tools and showed a mid-level of awareness about ethical concerns, but they recognized the importance of AI knowledge for their careers, emphasizing the need for AI integration in medical education.

Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility

BACKGROUND

The rapid advancement of artificial intelligence (AI) has revolutionized both medical education and healthcare by delivering innovative tools that enhance learning and improve overall outcomes. The study aimed to assess students' perceptions regarding the credibility and effectiveness of AI as a learning tool and to explore the dynamics of integrating AI in medical education.

METHODOLOGY

A cross-sectional study was carried out across medical colleges in Pakistan. A 26-question survey was developed using Google Forms from previously validated studies. The survey assessed demographics of participants, basic understanding of AI, AI as a learning tool in medical education and socio-ethical impacts of the use of AI. The data was analyzed using SPSS (v 26.0) to derive descriptive and inferential statistics.

RESULT

A total of 702 medical students aged 18 to 26 years (mean age 20.50 ± 1.6 years) participated in the study. The findings revealed a generally favorable attitude towards AI among medical students (80.3%), with the majority considering it an effective (60.8%) and credible (58.4%) learning tool in medical education. Students agreed that AI learning optimized their study time (60.3%) and provided up-to-date medical information (63.1%). Notably, 65.7% of students found AI more efficient in helping them grasp medical concepts compared to traditional tools like books and lectures, while 66.8% reported receiving more accurate answers to their medical inquiries through AI. The study highlighted that medical students view traditional tools as becoming increasingly outdated (59%), emphasizing the importance of integrating AI into medical education and creating dedicated AI tools (80%) for the medical education.

CONCLUSION

This study demonstrated that AI is an effective and credible tool in medical education, offering personalized learning experiences and improved educational outcomes. AI tools are helping students learn medical concepts by cutting down on study-time, providing accurate answers, and ultimately improving study outcomes. We recommend developing dedicated AI tools for medical education and their formal integration into medical curricula, along with appropriate regulatory oversight to ensure AI can enhance human abilities rather than acting as a replacement for humans.

Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study

Background

Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training.

Objective

This study aims to assess and compare medical AI-related attitudes among medical students in general medicine and in one of the visually oriented fields (pathology), along with illuminating their anticipated role of AI in the rapidly evolving landscape of AI-enhanced health care.

Methods

This was a cross-sectional study that used a web-based survey composed of a closed-ended questionnaire. The survey addressed medical students at all educational levels across the 5 public medical schools, along with pathology residents in 4 residency programs in Jordan.

Results

A total of 394 respondents participated (328 medical students and 66 pathology residents). The majority of respondents (272/394, 69%) were already aware of AI and deep learning in medicine, mainly relying on websites for information on AI, while only 14% (56/394) were aware of AI through medical schools. There was a statistically significant difference in awareness among respondents who consider themselves tech experts compared with those who do not (P=.03). More than half of the respondents believed that AI could be used to diagnose diseases automatically (213/394, 54.1% agreement), with medical students agreeing more than pathology residents (P=.04). However, more than one-third expressed fear about recent AI developments (167/394, 42.4% agreed). Two-thirds of respondents disagreed that their medical schools had educated them about AI and its potential use (261/394, 66.2% disagreed), while 46.2% (182/394) expressed interest in learning about AI in medicine. In terms of pathology-specific questions, 75.4% (297/394) agreed that AI could be used to identify pathologies in slide examinations automatically. There was a significant difference between medical students and pathology residents in their agreement (P=.001). Overall, medical students and pathology trainees had similar responses.

Conclusions

AI education should be introduced into medical school curricula to improve medical students' understanding and attitudes. Students agreed that they need to learn about AI's applications, potential hazards, and legal and ethical implications. This is the first study to analyze medical students' views and awareness of AI in Jordan, as well as the first to include pathology residents' perspectives. The findings are consistent with earlier research internationally. In comparison with prior research, these attitudes are similar in low-income and industrialized countries, highlighting the need for a global strategy to introduce AI instruction to medical students everywhere in this era of rapidly expanding technology.

S, Pattanayak D, Panda S, Mondal H. Exploring Radiology Postgraduate Students' Engagement with Large Language Models for Educational Purposes: A Study of Knowledge, Attitudes, and Practices

Background  The integration of large language models (LLMs) into medical education has received increasing attention as a potential tool to enhance learning experiences. However, there remains a need to explore radiology postgraduate students' engagement with LLMs and their perceptions of their utility in medical education. Hence, we conducted this study to investigate radiology postgraduate students' knowledge, attitudes, and practices regarding LLMs in medical education. Materials and Methods  A cross-sectional quantitative survey was conducted online on Google Forms. Participants from all over India were recruited via social media platforms and snowball sampling techniques. A previously validated questionnaire was used to assess knowledge, attitude, and practices regarding LLMs. Descriptive statistical analysis was employed to summarize participants' responses. Results  A total of 252 (139 [55.16%] males and 113 [44.84%] females) radiology postgraduate students with a mean age of 28.33 ± 3.32 years participated in the study. The majority of the participants (47.62%) were familiar with LLMs with their potential incorporation with traditional teaching-learning tools (71.82%). They are open to including LLMs as a learning tool (71.03%) and think that it would provide comprehensive medical information (62.7%). Residents take the help of LLMs when they do not get the desired information from books (46.43%) or Internet search engines (59.13%). The overall score of knowledge (3.52 ± 0.58), attitude (3.75 ± 0.51), and practice (3.15 ± 0.57) were statistically significantly different (analysis of variance [ANOVA], p  < 0.0001), with the highest score in attitude and lowest in practice. However, no significant differences were found in the scores for knowledge ( p  = 0.64), attitude ( p  = 0.99), and practice ( p  = 0.25) depending on the year of training. Conclusion  Radiology postgraduate students are familiar with LLM and recognize the potential benefits of LLMs in postgraduate radiology education. Although they have a positive attitude toward the use of LLMs, they are concerned about its limitations and use it only in limited situations for educational purposes.

Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review

Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology. They are also increasingly being utilized in studies on ophthalmology-related exams, particularly those containing multiple-choice questions (MCQs). This narrative review evaluates both the opportunities and the challenges of integrating chatbots into ophthalmology research, with separate assessments of studies involving open- and close-ended questions. While chatbots have demonstrated sufficient accuracy in handling MCQ-based studies, supporting their use in education, additional exam security measures are necessary. The research on open-ended question responses suggests that AI-based LLM chatbots could be applied across nearly all areas of ophthalmology. They have shown promise for addressing patient inquiries, offering medical advice, patient education, supporting triage, facilitating diagnosis and differential diagnosis, and aiding in surgical planning. However, the ethical implications, confidentiality concerns, physician liability, and issues surrounding patient privacy remain pressing challenges. Although AI has demonstrated significant promise in clinical patient care, it is currently most effective as a supportive tool rather than as a replacement for human physicians.

Readiness to Embrace Artificial Intelligence Among Medical Students in Saudi Arabia: A National Survey

BACKGROUND/OBJECTIVES

Artificial intelligence (AI) is rapidly reshaping healthcare, offering transformative potential for diagnostics, treatment, and patient management. Despite its growing significance, there is limited integration of AI education in medical curricula, raising concerns about the readiness of future healthcare professionals to utilize AI technologies. This study aims to evaluate the readiness of medical students in Saudi Arabia to embrace AI and to assess the current state of AI education, AI Application use, and future perspectives for medical students.

METHODS

a cross-sectional design was employed. It involved medical students from various regions of Saudi Arabia. Data were collected using an anonymous, online, structured, and validated tool from previous studies. The survey included sociodemographic information, details on AI education, the usage of AI applications, intended specialties, and a Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). The data were extracted and revised in an Excel sheet. Statistical analysis was conducted using the IBM SPSS computer program with appropriate statistical tests.

RESULTS

This study enrolled 572 medical students, with a mean age of 21.93 years. Most students were Saudi (99.0%), and 43.7% lived in the western region of Saudi Arabia. Most students attended a government medical college (97.41%), and 64.3% of students were in their clinical years. Only 14.5% of the students had received formal AI education, while 34.3% had participated in extracurricular AI training. The mean (SD) MAIRS-MS score was 68.39 (18.3), with higher scores associated with female students, those from the central region, and those with advanced English and computer technology skills (p < 0.001).

CONCLUSIONS

there is limited AI education and moderate AI readiness among medical students in Saudi colleges, with significant variability in terms of gender, region, and educational background. These findings underscore the need to integrate AI education into medical curricula to better prepare future physicians for AI-enabled healthcare systems.

Flexner's legacy and the future of medical education: Embracing challenge and opportunity

This editorial comments on the article by Alzerwi. We focus on the development course, present challenges, and future perspectives of medical education. Modern medical education is gradually undergoing significant and profound changes worldwide. The emergence of new ideas, methodologies, and techniques has created opportunities for medical education developments and brought new concerns and challenges, ultimately promoting virtuous progress in medical education reform. The sustainable development of medical education needs joint efforts and support from governments, medical colleges, hospitals, researchers, administrators, and educators.

Familiarity and Applications of Artificial Intelligence in Health Professions Education: Perspectives of Students in a Community-Oriented Medical School

Background and aim Medical students are expected to be familiar with artificial intelligence (AI) applications in healthcare. This cross-sectional study looked at the attitudes, thoughts, and understanding of healthcare students toward AI. Materials and methods During the academic year 2023-2024, medical students enrolled in the College of Medicine and Health Sciences (CMHS) at the Arabian Gulf University (AGU) were included in this study. A questionnaire was developed to evaluate their understanding and opinions regarding the use of AI in medical training. These data were gathered, categorized, and analyzed using the Statistical Package for Social Sciences (SPSS) version 29 (IBM Corp., Armonk, NY, US). Categorical variables were shown in the form of frequencies and percentages, whereas continuous variables were presented as mean and standard deviation (SD). Chi-square tests were utilized for comparing categorical variables. A p-value of <0.05 was considered statistically significant. Results The study found that n=41 (27%) of medical students are very familiar with AI applications while n=92 (60.5%) are somewhat familiar. Familiarity increases as students progress in their medical education, with senior clinical phase students more familiar than juniors. There was no significant difference in perceptions of AI application among medical phases. Familiarity with research methodology and studies increases familiarity with AI applications. Most students believe AI will have a positive impact on medical education, but perceptions vary by educational phase. Many students support integrating AI into curricula with 67 (44.1%) of students using AI applications, with a higher percentage in pre-clinical phases, likely due to application in research projects in this phase. Concerns were raised about AI impacting the human touch in medical practice and doctor-patient communication, as well as technical challenges faced by students when applying AI. Conclusion Arab Gulf medical students show positive attitudes toward AI applications in medical education. Tailored educational strategies are needed to optimize AI integration in medical practice and address concerns effectively.

Assessing the Utilization of Large Language Model Chatbots for Educational Purposes by Medical Teachers: A Nationwide Survey From India

Background Large language models (LLMs) are increasingly explored in healthcare and education. In medical education, they hold the potential to enhance learning by supporting personalized teaching, resource development, and student engagement. However, LLM use also raises concerns about ethics, accuracy, and reliance. Understanding how educators leverage LLMs can help assess their role and implications in medical education. Methods This cross-sectional online survey was conducted among medical teachers in India from December 2023 to March 2024. A validated questionnaire with acceptable internal consistency and test-retest reliability was used. It collected data on LLM chatbot usage patterns, as well as teachers' knowledge, attitudes, and practices regarding LLMs for educational purposes. Results A total of 396 medical teachers with an average teaching experience of 4.12±2.47 (minimum six months, maximum 13 years) years participated from different parts of India. The majority of the teachers heard about ChatGPT (OpenAI, San Francisco, CA, USA) (85%), followed by Copilot/Bing (Microsoft, Washington, DC, USA) (53%), and Gemini/Bard (Google, Mountain View, CA, USA) (45%) (p-value < 0.0001). However, 29% of the respondents never used it and 47% rarely use LLMs for educational purposes (p-value < 0.0001). The majority of the teachers use it for making any topic simple (55%), generating text for PowerPoint slides (55%), generating multiple-choice questions (MCQs) (52%), and finding answers to student's queries (35%). Knowledge (3.4±0.47) showed the highest score, followed by practice (3.3±0.81) and attitude (3.14±0.46) (p-value = 0.0023). Conclusion While awareness of LLMs was high among medical teachers in India, their actual usage for educational purposes remains limited. Despite recognizing the potential of LLMs for simplifying topics, generating teaching materials, and addressing student queries, a significant proportion of educators seldom integrate these technologies into their teaching practices. Institutions may provide training to help medical educators effectively integrate LLMs into teaching practices.

How do medical students perceive the role of artificial intelligence in management of gastroesophageal reflux disease?

BACKGROUND

Artificial intelligence (AI) has significantly revolutionized the diagnosis and treatment of various medical and surgical conditions, including gastroesophageal reflux disease (GORD). AI has the potential to enhance diagnostic and treatment capabilities, contributing to overall advancements in healthcare. The current study aimed to investigate the medical students' views regarding the use of AI in GORD management.

METHODS

An anonymous, self-administered questionnaire was distributed among undergraduate medical students of various grades within different national medical institutions. The questionnaire comprised three sections, addressing sociodemographic data, knowledge, and attitudes. Knowledge and attitudes were assessed through 5- and 7-item questionnaires, respectively. The knowledge scores constituted a scale of 0-5. This reflected varying levels of understanding. Categories include poor knowledge (two or less), moderate knowledge (three), and good knowledge (4 and 5). Attitudes were classified as negative, neutral, or positive based on 50% and 75% cutoff points, with scores below 50% indicating negative attitudes, 50-75% considered neutral, and scores above 75% reflecting positive attitudes.

RESULTS

A total of 506 medical students participated, including 273 females and 233 males, with a ratio of 1.2-1. The majority fell within the age range of 20-26 years. Additionally, 388 participants (76.7%) reported grade point averages (GPAs) higher than 4. Regarding knowledge, 9% demonstrated a poor score of knowledge, while 33% had a moderate knowledge score. However, 65% of the participating students held a neutral attitude toward the role of AI in GORD management, with 6.9% expressing a negative stance on the matter.

CONCLUSION

Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology. This may highlight the necessity for active involvement in AI education within the medical curricula.

Progress Achieved, Landmarks, and Future Concerns in Biomedical and Health Informatics

BACKGROUND

The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges.

METHODS

To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues.

RESULTS

Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches.

CONCLUSIONS

These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development.

Perspectives toward the application of Artificial Intelligence in anesthesiology-related practices in Saudi Arabia: A cross-sectional study of physicians views

Background and Aims

The use of Artificial Intelligence (AI) relies on computer science and large datasets, with the technology mimicking human intelligence as it makes logical decisions. This study aims to assess the perceptions and experiences of anesthesiology practitioners toward AI and identify its benefits to healthcare professionals and patients, along with current and future applications of AI.

Methods

This cross-sectional descriptive online survey study was disseminated to physicians who work in anesthesiology practice in Saudi Arabia. Descriptive statistics were used to report the characteristics of the respondents and summarize the results of the survey.

Results

There were 109 responses, with 85.32% being male, 35.78% being aged 40-49 years, and 69.72% being consultant anesthesiologists. The majority of participants (73.39%) believed that AI could be used in multiple settings related to anesthesiology practice. Participants also believed that AI could facilitate access to data (76.15%), enable precise decision-making (75.23%), reduce medical errors (55.04%), reduce workload and shortage of healthcare personnel (53.21%), and allow healthcare personnel to focus on more demanding cases (69.72%). In addition, the majority of participants believed that AI can be beneficial to patients, in which 69.72% believed that AI can improve patient access to care, 77.06% believed that AI can facilitate patient education, and 65.14% believed that AI can guide patients during treatment. Lastly, 70.64% believed that AI would be beneficial to anesthesiology practices in the future. However, 61.47% claimed that their workplace has no plan for adopting AI.

Conclusions

The anesthesiologists showed generally positive attitudes towards AI, in spite of its limited utilization and implementation challenges. Strong beliefs exist about AI's future potential in anesthesia care and postgraduate education.

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.

Ethical considerations for artificial intelligence use in nursing informatics

Artificial intelligence revolutionizes nursing informatics and healthcare by enhancing patient outcomes and healthcare access while streamlining nursing workflow. These advancements, while promising, have sparked debates on traditional nursing ethics like patient data handling and implicit bias. The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion. Mindful utilization of artificial intelligence coupled with an unwavering ethical commitment by nurses may transform the very essence of nursing.

Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study

BACKGROUND

ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area.

OBJECTIVE

The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants' attitudes toward the use of ChatGPT.

METHODS

A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses.

RESULTS

Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was "minimal" (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) "somewhat agreed" that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95% CI 1.426-1.564; P<.001 for all results).

CONCLUSIONS

Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs.

Artificial intelligence utilization in the healthcare setting: perceptions of the public in the UAE

Understanding the use of AI in healthcare is essential for the successful implementation of AI-driven healthcare solutions. The aim of this study was to evaluate public perception of AI utilization in healthcare settings. A validated questionnaire assessed general perceptions towards AI utilization, the use of AI by physician , and the use of AI by pharmacists . The study included 770 participants. The median perception score indicated an unfavorable attitude. Participants who had lower education level and those with no employment had a significantly lower perception scores than their counterpart. Participants who reported low income and those who visited the pharmacy five to ten times on average had a higher perception than their counterparts did. The reported negative perception necessitates the implementation of education campaigns to improve AI literacy and dispel any misconceptions and concerns, particularly among individuals with low education, high income, unemployment, and frequent pharmacy visits.

Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom's Taxonomy

Introduction

This research investigated the capabilities of ChatGPT-4 compared to medical students in answering MCQs using the revised Bloom's Taxonomy as a benchmark.

Methods

A cross-sectional study was conducted at The University of the West Indies, Barbados. ChatGPT-4 and medical students were assessed on MCQs from various medical courses using computer-based testing.

Results

The study included 304 MCQs. Students demonstrated good knowledge, with 78% correctly answering at least 90% of the questions. However, ChatGPT-4 achieved a higher overall score (73.7%) compared to students (66.7%). Course type significantly affected ChatGPT-4's performance, but revised Bloom's Taxonomy levels did not. A detailed association check between program levels and Bloom's taxonomy levels for correct answers by ChatGPT-4 showed a highly significant correlation (p<0.001), reflecting a concentration of "remember-level" questions in preclinical and "evaluate-level" questions in clinical courses.

Discussion

The study highlights ChatGPT-4's proficiency in standardized tests but indicates limitations in clinical reasoning and practical skills. This performance discrepancy suggests that the effectiveness of artificial intelligence (AI) varies based on course content.

Conclusion

While ChatGPT-4 shows promise as an educational tool, its role should be supplementary, with strategic integration into medical education to leverage its strengths and address limitations. Further research is needed to explore AI's impact on medical education and student performance across educational levels and courses.

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BACKGROUND

The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.

METHODS

This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.

RESULTS

From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

CONCLUSION

Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Attitude and Understanding of Artificial Intelligence Among Saudi Medical Students: An Online Cross-Sectional Study

Purpose

Artificial Intelligence is drastically used nowadays in healthcare, but little is known about the attitude and perception of medical students towards AI in Saudi Arabia. This study aimed to explore undergraduate medical student's views on AI, assessed their understanding of AI, and the level of confidence of using basic AI tools in the future.

Methods

This cross-sectional study invited 303 medical undergraduate students to complete an anonymous electronic survey, which consists of questions related to attitude, understanding and confidence of using basic AI tools. We examined the statistical association between the categorical variables by using Chi-square test.

Results

The results of the study indicate that eighty-seven percent of participants believed that AI will play significant role in healthcare. Thirty-eight percent respondents reported that they have an understanding of the basic computational principle of AI. 71.29% respondents agreed that teaching in AI would be favorable for their career. More than half of the participants were confident in using basic AI tools in the future, Male students (p = 0.00), 26-30 years old participants (p = 0.03), intern students (p = 0.00), and Imam Abdulrahman Bin Faisal University medical students (p = 0.04) had positive attitude of artificial intelligence. Male participants (p = 0.02), and intern students (p = 0.00) had the highest proportion of confidence in using basic healthcare AI tool. Nearly 14% students received training on AI. Participants who received training on AI reported better understanding of AI (p = 0.03), develops positive attitude towards teaching in AI (p = 0.05), more confidence in using basic healthcare AI tools (p = 0.05).

Conclusion

Saudi medical undergraduate students understand the significance of AI and demonstrated a positive attitude towards AI. Medical students training on AI should be expanded and improved to avoid threats for seeking jobs by adapting artificial intelligence.

Perceptions of Faculty and Students About Use of Artificial Intelligence in Medical Education: A Qualitative Study

BACKGROUND

Artificial intelligence (AI) implies using a computer to model intelligent behavior with minimal human intervention. With the advances of AI use in healthcare comes the need to reform medical education to produce doctors competent in AI use. Therefore, this qualitative study was conducted to explore faculty and students' perspectives on AI, their use of AI applications, and their perspective on its value and impact on medical education at a Saudi faculty of medicine.

METHODS

This qualitative study was conducted at the Faculty of Medicine, Jazan University in Saudi Arabia. A direct interview was held with 11 faculty members, and six focus group discussions were conducted with students from the second to sixth year (34 students). Data were collected using semi-structured open-ended interview questions based on relevant literature.

FINDINGS

Most respondents (91.11%) believed AI systems would positively impact medical education, especially in research, knowledge gain, assessment, and simulation. However, ethical concerns were raised about threats to academic integrity, plagiarism, privacy/confidentiality issues, and AI's lacking cultural sensitivity. Faculty and students felt a need for training on AI use (80%) and that the curriculum could adapt to integrate AI (64.44%), though resources were seen as currently needing to be improved.

CONCLUSION

AI's potential to enhance medical education is generally viewed positively in the study, but ethical concerns must be addressed. Integrating AI into medical education programs requires adequate resources, training, and curriculum adaptation. There is still a need for further research in this area to develop comprehensive strategies.

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.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan

INTRODUCTION

The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions.

METHODS

An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores.

RESULTS

Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps.

CONCLUSIONS

This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.

Assessing the Utilization of Large Language Models in Medical Education: Insights From Undergraduate Medical Students

Background Artificial intelligence (AI) has the potential to be integrated into medical education. Among AI-based technology, large language models (LLMs) such as ChatGPT, Google Bard, Microsoft Bing, and Perplexity have emerged as powerful tools with capabilities in natural language processing. With this background, this study investigates the knowledge, attitude, and practice of undergraduate medical students regarding the utilization of LLMs in medical education in a medical college in Jharkhand, India. Methods A cross-sectional online survey was sent to 370 undergraduate medical students on Google Forms. The questionnaire comprised the following three domains: knowledge, attitude, and practice, each containing six questions. Cronbach's alphas for knowledge, attitude, and practice domains were 0.703, 0.707, and 0.809, respectively. Intraclass correlation coefficients for knowledge, attitude, and practice domains were 0.82, 0.87, and 0.78, respectively. The average scores in the three domains were compared using ANOVA. Results A total of 172 students participated in the study (response rate: 46.49%). The majority of the students (45.93%) rarely used the LLMs for their teaching-learning purposes (chi-square (3) = 41.44, p < 0.0001). The overall score of knowledge (3.21±0.55), attitude (3.47±0.54), and practice (3.26±0.61) were statistically significantly different (ANOVA F (2, 513) = 10.2, p < 0.0001), with the highest score in attitude and lowest in knowledge. Conclusion While there is a generally positive attitude toward the incorporation of LLMs in medical education, concerns about overreliance and potential inaccuracies are evident. LLMs offer the potential to enhance learning resources and provide accessible education, but their integration requires further planning. Further studies are required to explore the long-term impact of LLMs in diverse educational contexts.