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

Artificial intelligence in respiratory care: knowledge, perceptions, and practices-a cross-sectional study

Background

Artificial intelligence (AI) is reforming healthcare, particularly in respiratory medicine and critical care, by utilizing big and synthetic data to improve diagnostic accuracy and therapeutic benefits. This survey aimed to evaluate the knowledge, perceptions, and practices of respiratory therapists (RTs) regarding AI to effectively incorporate these technologies into the clinical practice.

Methods

The study approved by the institutional review board, aimed at the RTs working in the Kingdom of Saudi Arabia. The validated questionnaire collected reflective insights from 448 RTs in Saudi Arabia. Descriptive statistics, thematic analysis, Fisher's exact test, and chi-square test were used to evaluate the significance of the data.

Results

The survey revealed a nearly equal distribution of genders (51% female, 49% male). Most respondents were in the 20-25 age group (54%), held bachelor's degrees (69%), and had 0-5 years of experience (73%). While 28% had some knowledge of AI, only 8.5% had practical experience. Significant gender disparities in AI knowledge were noted (p < 0.001). Key findings included 59% advocating for basics of AI in the curriculum, 51% believing AI would play a vital role in respiratory care, and 41% calling for specialized AI personnel. Major challenges identified included knowledge deficiencies (23%), skill enhancement (23%), and limited access to training (17%).

Conclusion

In conclusion, this study highlights differences in the levels of knowledge and perceptions regarding AI among respiratory care professionals, underlining its recognized significance and futuristic awareness in the field. Tailored education and strategic planning are crucial for enhancing the quality of respiratory care, with the integration of AI. Addressing these gaps is essential for utilizing the full potential of AI in advancing respiratory care practices.

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.

Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study

Objectives

The application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes of healthcare professionals toward its applications in nursing. This study aimed to explore the knowledge, attitudes, and concerns of healthcare professionals, AI-related professionals, and others in China toward AI in nursing.

Methods

We conducted an online cross-sectional study on nursing students, nurses, other healthcare professionals, AI-related professionals, and others in China between March and April 2024. They were invited to complete a questionnaire containing 21 questions with four sections. The survey followed the principle of voluntary participation and was conducted anonymously. The participants could withdraw from the survey at any time during the study.

Results

This study obtained 1,243 valid questionnaires. The participants came from 25 provinces and municipalities in seven regions of China. Regarding knowledge of AI in nursing, 57% of the participants knew only a little about AI, 4.7% did not know anything about AI, 64.7% knew only a little about AI in nursing, and 13.4% did not know anything about AI in nursing. For attitudes toward AI in nursing, participants were positive about AI in nursing, with more than 50% agreeing and strongly agreeing with each question on attitudes toward AI in nursing. Differences in the numbers of participants with various categories of professionals regarding knowledge and attitudes toward AI in nursing were statistically significant (p < 0.05). Regarding concerns and ethical issues about AI in nursing, every participant expressed concerns about AI in nursing, and 95.7% of participants believed that it is necessary to strengthen medical ethics toward AI in nursing.

Conclusion

Nursing students and healthcare professionals lacked knowledge about AI or its application in nursing, but they had a positive attitude toward AI. It is necessary to strengthen medical ethics toward AI in nursing. The study's findings could help develop new strategies benefiting healthcare.

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.

Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use

BACKGROUND

In recent years, the use of artificial intelligence (AI) in education has increased worldwide. The launch of the ChatGPT-3 posed great challenges for higher education, given its popularity among university students. The present study aimed to analyze the attitudes of university students toward the use of ChatGPTs in their academic activities.

METHOD

This study was oriented toward a quantitative approach and had a nonexperimental design. An online survey was administered to the 499 participants.

RESULTS

The findings of this study revealed a significant association between various factors and attitudes toward the use of the ChatGPT. The higher beta coefficients for responsible use (β=0.806***), the intention to use frequently (β=0.509***), and acceptance (β=0.441***) suggested that these are the strongest predictors of a positive attitude toward ChatGPT. The presence of positive emotions (β=0.418***) also plays a significant role. Conversely, risk (β=-0.104**) and boredom (β=-0.145**) demonstrate a negative yet less decisive influence. These results provide an enhanced understanding of how students perceive and utilize ChatGPTs, supporting a unified theory of user behavior in educational technology contexts.

CONCLUSION

Ease of use, intention to use frequently, acceptance, and intention to verify information influenced the behavioral intention to use ChatGPT responsibly. On the one hand, this study provides suggestions for HEIs to improve their educational curricula to take advantage of the potential benefits of AI and contribute to AI literacy.

Perspectives of Pharmacy Students on Ethical Issues Related to Artificial Intelligence: A Comprehensive Survey Study

Background

The integration of artificial intelligence (AI) into pharmacy education and practice holds the potential to advance learning experiences and prepare future pharmacists for evolving healthcare practice. However, it also raises ethical considerations that need to be addressed carefully. This study aimed to explore pharmacy students' attitudes regarding AI integration into pharmacy education and practice.

Methods

A cross-sectional design was employed, utilizing a validated online questionnaire administered to 702 pharmacy students from diverse demographic backgrounds. The questionnaire gathered data on participants' attitudes and concerns regarding AI integration, as well as demographic information and factors influencing their attitudes.

Results

Most participants were female students (72.8%), from public universities (55.6%) and not working (64.2%). Participants expressed a generally negative attitude toward AI integration, citing concerns and barriers such as patient data privacy (62.0%), susceptibility to hacking (56.2%), potential job displacement (69.3%), cost limitations (66.8%), access (69.1%) and the absence of regulations (48.1% agree), training (70.4%), physicians' reluctance (65.1%) and patient apprehension (70.8%). Factors including country of residence, academic year, cumulative GPA, work status, technology literacy, and AI understanding significantly influenced participants' attitudes (p < 0.05).

Conclusion

The study highlights the need for comprehensive AI education in pharmacy curricula including related ethical concerns. Addressing students' concerns is crucial to ensuring ethical, equitable, and beneficial AI integration in pharmacy education and practice.

Evaluating the understanding of the ethical and moral challenges of Big Data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study

AIMS

To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners.

METHODS

We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants' responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA.

RESULTS

We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of "broad consent", respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI's abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare.

CONCLUSION

Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology.