Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam

Evaluation of Awareness, Perception and Opinions Toward Artificial Intelligence Among Pharmacy Students

Background: Artificial intelligence (AI) helps to develop personalized medication therapy and regimens. It improves the patient care system. A cross-sectional study used and included pharmacy students, using validated survey questions. Objective: This study aimed to evaluate awareness, perception and opinion toward AI among pharmacy students. Design: This is a cross-sectional study (survey-based). Methods: A cross-sectional survey distribution among students in different levels of the college of pharmacy at National University (NU). The questions were classified to measure the variation of demographics, awareness, perceptions and opinions toward Artificial Intelligence (AI). Results: The results showed that more than 50% of pharmacy students are familiar with the uses of AI and know it's important in scientific research, 46.4% have a basic understanding of AI technologies. However more than 75% don't know the applications of AI used in pharmacy practice, 50.6 % don't know AI can support therapeutic diagnosis and 57 % don't know its importance in pharmacy education. A high perception was shown toward AI in facilitating pharmacy access to information (84.2%) and patients' access to the service (80.8%). In addition, 92% suggested that AI training is needed and 86.1 % recommended using AI in scientific research. The conclusion of this study identified the needs for awareness toward AI, and the important role of AI for education in pharmacy and health communities.

Evaluation of Health Science Students' Knowledge, Attitudes, and Practices Toward Artificial Intelligence in Northern Saudi Arabia: Implications for Curriculum Refinement and Healthcare Delivery

Background and Aim

As the integration of artificial intelligence (AI) in healthcare delivery becomes increasingly prevalent, understanding the knowledge, attitudes, and practices of health science students towards AI is crucial. However, limited evidence exists regarding the readiness of health science students, particularly in northern Saudi Arabia (KSA), to integrate AI into their future practices, highlighting the need for focused evaluation. We evaluated northern Saudi health science students' knowledge, attitude, practice, and associated factors toward AI.

Participants and Methods

The present cross-sectional study was conducted among 384 health science students aged 18 years and above from Jouf University, KSA. The study employed a validated data collection form with four sections: demographics, knowledge (AI principles and applications), attitudes (perceptions and ethical concerns), and practices (usage and confidence in AI tools). The three domains' scores were categorized as low (<60%), medium (60-80%) and high (>80%) based on their total scores. We utilized Spearman correlation test to ascertain the strength and direction of correlation among each subscale. Additionally, multivariate analysis was employed to identify associated factors.

Results

The present study demonstrated low knowledge, attitude, and practices among 55.7%, 37.0%, and 50.3% of health science students. We observed a positive correlation between knowledge and attitude (rho = 0.451, p = 0.001), knowledge and practice (rho = 0.353, p = 0.001), and attitude and practice (rho = 0.651, p = 0.001). Knowledge (p = 0.001) and practice (p = 0.002) were significantly higher among the students who participated in a formal AI training program. Females had a significantly higher level of attitude (p = 0.001) and practice (p = 0.030) than males.

Conclusion

In light of these findings, refining the curriculum to incorporate AI emerges as a critical strategy for addressing gaps in AI knowledge, attitudes, and practices among health science students. Therefore, formal and integrated training programs tailored to suit the local setting can effectively prepare health science students to adopt AI technologies in ways that enhance patient care.

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.

Integration of Artificial Intelligence in Pediatric Education: Perspectives from Pediatric Medical Educators and Residents

OBJECTIVES

The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum.

METHODS

An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree.

RESULTS

A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools.

CONCLUSIONS

The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.

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.

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.