Abstract
Background and Objectives
The objective of this cross-sectional study is to investigate medical students’ and interns’ perspectives on AI and the influence that AI has on medical education in the western region of Saudi Arabia. The objectives include determining the awareness of medical students, the various uses of AI in medical education, and recognizing both the positive and negative effects on educational settings.
Materials and Methods
This is a cross-sectional study, using a validated online questionnaire that was distributed to undergraduate medical students as well as medical interns.
Results
A total of 375 medical students and interns have filled out the surveys. We found that the majority of participants, specifically 346 individuals (92.3%), were acquainted with the notion, whereas only 29 participants (7.7%) had no understanding of it. A substantial number of participants, 153 (40.8%), indicated favorable opinions regarding the impact of AI on their educational experience, while 158 (42.1%) were unfavorable, and 64 (17.1%) remained neutral. However, 125 individuals (33.3%) disagreed with the assertion on that “I believe AI can have a negative impact on medical education,” while the majority 129 (34.4%) remained neutral, and 121 (32.3%) expressed a positive opinion. When queried about the potential impact of AI on regular clinical practice in the future, a majority of 217 individuals (57.9%) expressed agreement. By comparison, a total of 142 participants, accounting for 37.9% of the sample, indicated uncertainty, while a mere 16 participants held the belief that AI will not have a significant impact in the future.
Conclusions
These findings can assist educational institutions and policymakers in adapting curricula and resources to maximize the benefits of AI in medical education while addressing any potential concerns that may arise as a result of its use.
Keywords: Artificial Intelligence, medical students, intern, education, impact, KSA
Introduction
Artificial intelligence (AI) is a multidisciplinary field that combines computer science and linguistics with the objective of developing machines that possess the ability to perform tasks typically associated with human intelligence. 1 These tasks encompass cognitive abilities such as learning, adaptability, rationalization, comprehension, problem-solving, decision-making processes, and the ability to grasp abstract concepts. Additionally, they involve responsiveness to intricate human attributes such as attention, emotion, and creativity. 2
AI has a major impact on the advancement of numerous industries, including the production sector, economics, education, and health. AI is advancing expeditiously, and its use in medicine is growing.3,4 It is becoming more and more common in several medical specialties, such as pathology, 5 dermatology, 4 and ophthalmology. 6
The result of such rapid growth is notable in various applications, including chatbots such as ChatGPT, JasperChat, DialoGPT, Replika, and Poe. 7 Nowadays, AI tools are being developed to assess a wide range of health data, including data from patients and medical literature, as well as clinical, behavioral, environmental, and pharmaceutical information.8,9 Along with a number of other improvements, diagnosis and treatment can now be carried out more rapidly and accurately, radiological techniques are becoming better, drug research is easier, and more customized therapies are achievable.9–11 In medical education, AI functions as a teaching assistant, aids personal learning, provides quick access to information, and generates case scenarios, among other roles.
To our knowledge, most studies on AI have concentrated on the applications of it in medical practice or simply the perspectives of students on AI and whether it will replace their jobs in the future. A study done in Riyadh aimed to evaluate awareness, perceptions, and opinions towards AI among pharmacy undergraduate students at King Saud University (KSU) showed that pharmacy students have good awareness and positive perceptions, Besides, it indicated that there is a need for more education and training in the field of AI. 12 Another study conducted with 390 medical students in the US found that they recognised the importance of AI in medicine. 13 In addition to this, a study done in Kuwait aimed to investigate students’ perceptions of adopting AI systems in medical education showed that they had positive perceptions and believe that learning about AI will benefit their careers. 14 Apart from this, many studies on the use of AI applications in various medical specialties have been published, most notably in diagnostic radiology, pathology, cardiovascular medicine, ophthalmology, dentistry, and dermatology. 14 As in the case of the AI impact on medical practice, a study was completed in the UK to investigate medical students’ understanding of AI and career intentions towards radiology, half of them reported that AI made them less likely to consider a career in radiology. 15 An additional study conducted in Korea sought to investigate neuroradiologists’ current expectations and clinical adoption of AI software. The majority of respondents had used AI software and were enthusiastic about incorporating AI into clinical practice. 16
As shown previously, none of the previous studies measured the effect of AI on medical education, and because AI is becoming involved in every aspect of our lives, it is imperative to gain insight into students’ and interns’ perceptions of AI and its impact on medical education in order to ensure optimal utilization of it, along with assessment of medical students’ and interns’ awareness and their understanding of its ethical implications. Hence, this study aimed to evaluate the perception, and both the positive and negative impact of AI on the academic journey. As well as to measure awareness of the diverse applications of AI in the medical education among medical students and interns, in the western region, KSA. This study is one of the initial studies that explore the perception and influence of AI on the medical education of students and interns in the western region of Saudi Arabia.
Methodology
Study Design, Setting, and Population
The study's framework was illustrated in Figure 1.This study follows the Consensus-Based Checklist for Reporting of Survey Studies (CROSS) guidelines to ensure methodological transparency. 17 A cross-sectional survey was done among undergraduate medical students and interns in the western region, Data were collected by researchers through an online self-administered survey distributed via social media applications between February 27, 2024, and April 27, 2024. The participants were selected using non-probability convenient sampling technique. Regarding questionnaire was adopted from other studies after subjecting them to a few modifications to suit our objectives, it is fully provided in the article. All students, including those in their first year to their final year, as well as interns, who are studying medicine in the western region, were included. Students from other regions along with students and intern from other health specialists were excluded from the study. Prior to data collection, the study received approval from the ethics council of the College of Medicine, Taibah University, located in Madinah, Saudi Arabia. Before commencing the trial, all participants provided their consent after receiving comprehensive information. In addition, the participants were assured that the information would solely be used for research purposes and that the study would be conducted with complete confidentiality. Participants have the opportunity to withdraw from the study at their desire. By requiring participants to provide a unique identifier, such as an email address or phone number, we ensure response accuracy and that everyone responds only once. Incomplete responses were not possible, as all questions were mandatory for submission. As a result, there were no missing data or lost samples. Additionally, the survey consisted of multiple-choice questions, to minimize outliers by restricting responses to the provided options. To mitigate response bias, we used neutral and well-structured questions to avoid leading respondents.
Figure 1.
Study Framework Overview.
Study Sample
We obtained the number of medical students and interns in the western region from all medical colleges in the western region (private colleges included after contacting them, the total number was 9500 students and interns. The sample was calculated using Epi info TM software, and the calculation of sample size was 370. We successfully exceeded this target in our questionnaire, thereby reaching data saturation for this study.
Designing of the Questionnaires
The survey was designed using Google Form in English. It consists of four sections. The first section entailed demographic questions (gender, age, city of residence, and year of medical education). The second section includes 4 multiple choice questions to investigate medical students’ knowledge about AI. The third section focused on students’ experience with AI in education including five-points Likert statements (1 = strongly disagree to 5 = strongly agree) whereby participants rate their agreement with statements related to their opinions about AI, and its effects on their educational journey. The last section contains questions that inspect participants’ perceptions regarding AI role in the future of medical practice and education.
Cronbach's alpha was used to assess the reliability of the study tool, for the section on the participants’ perception of AI. The reliability coefficient was found to be 0.836, which is considered high and indicates the validity of this section for application. For other parts of the questionnaire, Cronbach's alpha was not suitable. Instead, we utilized a questionnaire derived from previously published research in the same field. This questionnaire was reviewed by three consultants, and any necessary modifications were applied. Furthermore, a preliminary investigation was carried out among a randomly chosen subset of medical students and interns.
Assessment of Attitude (perception)
Perception was assessed through a Likert scale ranging from 1 to 5 (1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; 5 = strongly agree). The perception assessment included six statements addressing various aspects of AI. For simplicity, the scores were reported on a scale from +1 (representing strongly agree and agree) to −1 (representing strongly disagree and disagree), with the midpoint (0) indicating a neutral response. Participants with positive scores were classified into the favorable perception category, while those with zero were neutral perceptions, negative scores were categorized as having unfavorable.
Statistical Analysis
After gathering the data, the questionnaire was examined to ensure its accuracy and completeness. The data were analysed using the IBM SPSS statistical software package, specifically version 29.0.2.0. A descriptive analysis was employed to calculate the frequencies and percentages of several variables. The perception among the studied participants toward AI was compared according to participants’ characteristics using chi-square test. A p-value of ≤ 0.05 was considered statistically significant.
Results
Sociodemographic Characteristics of Participants
A total of 375 medical students and interns successfully completed the surveys. Out of the total, there were 156 (41.6%) males and 219 (58.4%) females. The survey participants consisted of 215 (57.3%) individuals between the ages of 18 and 22, 150 (40.0%) individuals between the ages of 23 and 25, and 10 (2.7%) individuals above the age of 25.
Among Western universities, participants from Taibah University were 100 (26.7%), Umm Al-Qura University were 69(18.4%), King Abdulaziz University were 40 (10.7%), King Saud bin Abdulaziz University for Health Sciences-Jeddah were 38 (10.1%), Taif University were 36 (9.6%), Jeddah University were 30 (8.0%), Al-Rayan University were 23(6.1%), Ibn Sina National College were 16 (4.3%), Batterjee College were 13 (3.5%) and Fakeeh College were 10 (2.7%).
A total of 26 (6.9%) of the participants were interns, 58 (15.5%) were in their sixth year, 77(20.5%) were in their fifth year, 87(23.2%) were in their fourth year, 59 (15.7%) were in their third year, 40(10.7%) were in their second year, and 28 (7.5%) were in their first year. With the majority of participants were fourth-year medical students. Table 1 provides all of the participants’ sociodemographic details.
Table 1.
Sociodemographic Characteristics of Participants (n = 375).
| Variable | Frequency (n) | % |
|---|---|---|
| Gender | ||
| Male | 156 | 41.6 |
| Female | 219 | 58.4 |
| Age | ||
| 18–22 | 215 | 57.3 |
| 23–25 | 150 | 40.0 |
| Above 25 | 10 | 2.7 |
| Medical school | ||
| Taibah University | 100 | 26.7 |
| Umm Al-Qura University | 69 | 18.4 |
| King Abdulaziz University | 40 | 10.7 |
| King Saud bin Abdulaziz University for Health Sciences-Jeddah | 38 | 10.1 |
| Taif University | 36 | 9.6 |
| Jeddah University | 30 | 8.0 |
| Al-Rayan University | 23 | 6.1 |
| Ibn Sina National College | 16 | 4.3 |
| Batterjee College | 13 | 3.5 |
| Fakeeh College | 10 | 2.7 |
| Year of study | ||
| Internship | 26 | 6.9 |
| Sixth year | 58 | 15.5 |
| Fifth year | 77 | 20.5 |
| Fourth year | 87 | 23.2 |
| Third year | 59 | 15.7 |
| Second year | 40 | 10.7 |
| First year | 28 | 7.5 |
Background of AI Among Medical Students and Interns
Upon investigating the level of awareness of AI, we found that the majority of participants 346 (92.3%) were familiar with the concept, while just 29 (7.7%) students had no knowledge about it. Additionally, we found that the bulk of participants 270 (72.0%) had not received any formal education on the AI topic, whereas only 105 individuals had received education on AI (Figure 2).
Figure 2.
Summary of the Participants’ Familiarity with AI.
When assessing the sources from which participants acquire their experience, the majority of responses 334 (31.7%) indicate that they gain it from media platforms such as television, YouTube, and Twitter. This is followed by family and friends, with 239 (22.7%) responses. Online forums account for 121 (11.5%) responses, while research projects contribute 89 (8.4%) responses. Professors and doctors are referred to as a source of experience in 69 (6.5%) responses, formal lectures in 60 (5.7%) responses, conferences in 55 (5.2%) responses, books in 47 (4.5%) responses, and peer-reviewed articles in 41 (3.9%) responses.
The participants showed interest in various topics related to AI in medicine. The uses of AI in medicine received 238 (19.8%) responses, followed by discussions on the strengths and weaknesses of utilizing AI in medicine with 229 (19.1%) responses. The use of AI in medical research was highlighted by 212 (17.7%) participants, while the ethical considerations of using AI in medicine were discussed by 157 (13.1%) participants. Additionally, 152 (12.7%) participants discussed which aspects of a physician's job can be replaced by AI and which cannot. The most recent and significant AI health innovations/research in the top 3 listed specialties received 120 (10%) responses, and the global health implications of AI were discussed by 91 (7.6%) participants. All details of the participants’ knowledge about AI are shown in Table 2.
Table 2.
Participants’ Knowledge about AI (n = 375).
| Variable | Frequency (n) | % |
|---|---|---|
| Where did you gain exposure to AI? | ||
| Media (television, YouTube, Twitter) | 334 | 31.7 |
| Formal lectures | 60 | 5.7 |
| Research projects | 89 | 8.4 |
| Books | 47 | 4.5 |
| Conference | 55 | 5.2 |
| Family and Friends | 239 | 22.7 |
| Professors/doctors | 69 | 6.5 |
| Peer-reviewed articles | 41 | 3.9 |
| Online forums | 121 | 11.5 |
| What are some specific topics within AI in medicine you would MOST be interested in? | ||
| Strengths and weaknesses of using AI in medicine | 229 | 19.1 |
| When to use AI in medicine? | 238 | 19.8 |
| Most recent and significant AI health innovation/research in my top 3 listed specialties | 120 | 10 |
| Ethics of AI | 157 | 13.1 |
| What aspects of a physician's job can be replaced with AI, and which can’t | 152 | 12.7 |
| AI in medical research | 212 | 17.7 |
| Global health implications of AI | 91 | 7.6 |
Participants Experience with AI
Figure 3 presents the frequency distribution of participants’ perceptions toward artificial intelligence (AI) in medical education. Regarding the belief that AI can positively impact medical education, 158 (42.1%) of participants held an unfavorable view, 64 individuals (17.1%) were neutral, and 153 individuals (40.8%) had a favorable perception. In contrast, when asked about the negative impact of AI, 125 (33.3%) disagreed, 129 (34.4%) were neutral, and 121 (32.3%) expressed a favorable stance. The ethical implications of AI in medical education evoked mixed responses, with 112 (29.9%) of participants having an unfavorable opinion, 116 (30.9%) remaining neutral, and 149 (39.2%) expressing concern.
Figure 3.
Perception of the Participants about AI.
A notable portion of participants accounting for 170 (45.3%) found training on AI concepts helpful for their medical education, while 133 respondents (35.5%) expressed an unfavorable opinion on this, and only 72 respondents (19.2%) remained neutral. Regarding the potential distraction of AI-related topics from the medical curriculum, opinions were more divided, with 119 respondents (31.7%) had unfavorable opinions, 128 (34.1%) were neutral, and 128 (34.1%) had favorable responses. Lastly, when asked if using AI for study would prepare them for its clinical use, 86 individuals (23.2%) viewed it unfavorably, 127 (33.9%) were neutral, and majority with 162 respondents (43.2%) were favorable. This distribution indicates a general interest in AI's potential benefits for medical education, alongside concerns about its ethical implications and the possible disruption of the traditional curriculum.
Table 3 presents a comparison of favorable perceptions toward artificial intelligence (AI) across various personal characteristics of the study participants. The age groups (18‒22, 23‒25, and >25 years) showed no significant differences in AI perceptions (p = .16). Similarly, the differences between male and female participants in favorable perceptions were not statistically significant (p = .18). For the educational year, there was no significant difference between the first three years, second three years, and intern groups in their favorable AI perceptions (p = .96). However, a significant difference emerged when comparing perceptions by university (p = .04). Participants from King Abdul-Aziz University (60.0%) and Taif University (52.8%) had higher proportions of favorable AI perceptions compared to participants from other universities, such as Taibah (34.0%) and Um Al-Qura (40.6%).
Table 3.
Comparison of Favorable Perception Toward AI by the Personal Characteristics of the Studied Participants.
| Characteristics | Favorable perception N = 164 n (%) |
Unfavorable and neutral perception N = 211 n (%) |
p |
|---|---|---|---|
| Age in years | |||
| 18–22 | 87 (40.5) | 128 (59.5) | .16 |
| 23–25 | 74 (49.3) | 76 (50.7) | |
| >25 | 3 (30.0) | 7 (70.0) | |
| Sex | |||
| Male | 62 (39.7) | 94 (60.3) | .18 |
| Female | 102 (46.6) | 117 (53.4) | |
| University | |||
| Taibah | 34 (34.0) | 66 (66.0) | .04** |
| Um Al-Qura | 28 (40.6) | 41 (59.4) | |
| King Abdul-Aziz | 24 (60.0) | 16 (40.0) | |
| Taif | 19 (52.8) | 17 (47.2) | |
| Others | 59 (45.4) | 71 (54.6) | |
| Educational year | |||
| First three years | 55 (43.3) | 72 (56.7) | .96 |
| Second three years | 97 (43.7) | 125 (56.3) | |
| Interns | 12 (46.2) | 14 (53.8) |
Denotes significant.
According to our survey, the most common areas where AI is used for medical educational purposes are in paraphrasing and grammar checking with a total of 256 (12%) responses. Following this, in order of using AI for summarizing medical literature 227 (11%), research assistance 221 (10%), generating case scenarios 169 (8%), writing formal emails 156 (7%), personalized learning by answering the question, provide feedback and create Study plans 155 (7%), explaining complex concepts and terminology 149 (7%), revision and exam preparation 143 (7%), updating knowledge 140 (7%), writing CVs (curriculum vitae) 123 (6%), medical calculations and formulas 122 (6%), preparing for the SMLE (Saudi Medical Licensing Exam) 105 (5%), clinical documentation assistance 95 (4%), and lastly offering procedural guidance 79 (4%). Table 4 summarizes the participants responses regarding AI's beneficial uses in education.
Table 4.
Participants’ Experience with AI (n = 375).
| Which aspects of AI integration in medical education do you find most beneficial? | Frequency (n) |
% |
|---|---|---|
| Paraphrasing and grammar checking | 256 | 12 |
| Research assistance | 221 | 10 |
| Summarizing medical literature | 227 | 11 |
| Writing formal emails | 156 | 7 |
| Writing CVs (Curriculum Vitae) | 123 | 6 |
| Personalized learning by answering questions, providing feedback, and creating study plans. | 155 | 7 |
| Explaining complex concepts and terminology | 149 | 7 |
| Updating knowledge | 140 | 7 |
| Preparing for the SMLE (Saudi Medical Licensing Exam) | 105 | 5 |
| Revision and exam preparation | 143 | 7 |
| Generating case scenarios | 169 | 8 |
| Offering procedural guidance | 79 | 4 |
| Medical calculations and formulas | 122 | 6 |
| Clinical documentation assistance | 95 | 4 |
Participants’ Opinion Regarding AI
A summary of the positive perception of participants’ opinions on AI shown in Table 5. The majority of respondents 284 (27%), agreed that AI helps them save time when obtaining information. Additionally, 253 (24%) respondents believed that AI allows effortless access to knowledge from various sources. Furthermore, 187 (18%) of respondents agreed that AI enhances the enjoyment of their learning journey. In terms of academic performance, 170 (16%) respondents believed that AI contributes to grade improvement, while 169 (16%) agreed that AI assists in the development of learning tools that aid their progress.
Table 5.
Participants’ Opinion Regarding AI (n = 375).
| Variables | Frequency (n) |
% |
|---|---|---|
| Could you select a statement that you believe to be true from the options provided? | ||
| AI helps improve my grades | 170 | 16 |
| AI helps me spend less time obtaining information | 284 | 27 |
| AI allows me to access knowledge from various sources effortlessly | 253 | 24 |
| AI helps me develop learning tools that aid my progress | 169 | 16 |
| AI makes learning more enjoyable | 187 | 18 |
| AI may oversimplify complex subjects, leading to a superficial understanding of concepts | 209 | 20.6 |
| AI has the potential for errors and bias | 240 | 23.6 |
| AI can lead to overreliance and a loss of critical thinking skills and problem-solving abilities | 201 | 19.8 |
| AI may provide inaccurate and biased information | 197 | 19.4 |
| AI could reduce interpersonal and communication skills | 168 | 16.6 |
Regarding participants’ negative perceptions of AI. Two hundred and forty (23.6%) of participants chose that AI has the potential for errors and bias, 209 (20.6%) selected that AI may oversimplify complex subjects, leading to a superficial understanding of concepts, 201 (19.8%) agreed with the idea that AI can lead to overreliance and a loss of critical thinking skills and problem-solving abilities, 168 (16.6%) chose that AI could reduce interpersonal and communication skills, and 197 (19.4%) selected that AI may provide inaccurate and biased information. In the last question of the opinions section, we addressed an open question to the participants, which was “How do you feel about using AI-driven platforms to enhance your learning ing experience?, We received a variety of responses, which will be further explored in the discussion section.
AI in the Future
When we asked whether AI will have a substantial impact on ordinary clinical practice in the future, a majority of them 217 (57.9%) agreed. On the other hand, 142 (37.9%) participants expressed uncertainty, while only 16 (4.3%) believed that AI will not play a big role in the future.
One of the survey questions aimed to identify the medical specialties that respondents believed would be most impacted by AI. As shown in Figure 4, radiology received the highest percentage of responses, with a significant 240 (10.1%). This was followed by ENT with 210 (8.8%) and preventive medicine with 179 (7.5%). Other specialties included family medicine with 126 (5.3%), pathology with 124 (5.2%), internal medicine with 97 (4.1%), hematology with 92 (3.9%), urology with 92 (3.9%) and Pediatrics with 88 (3.7%). Moreover, 87 (3.7%) participants chose dermatology, in addition to ophthalmology with 87 (3.5%), ICU 84 (3.5%), neurology 81(3.4%), oncology 75 (3.2%), general surgery 73 (3.1%), anesthesiology 72 (3%), forensic medicine 71 (3%), plastic surgery 70 (3%), neurosurgery 66 (2.8%), emergency medicine 65 (2.7%), cardiac surgery-cardiology 53 (2.2%), orthopedics 53 (2.2%), pediatric neurology 42 (1.8%), pediatric surgery 42 (1.8%), and OB/GYN 41 (1.7%).
Figure 4.
Participants’ Distribution of Specialties Based on the Most Affected by AI in the Future.
In order to enhance our survey, we asked two open-ended questions to the participants so they could express their opinions freely about AI in the future. The first one was about how AI will shape the future of medical education and practice in Saudi Arabia. We found that our participants engaged actively as we received plenty of answers, that will be further analysed in the discussion section. In the second question we addressed how can AI implementation be improved to better serve medical students and interns? We have received a diverse range of replies about 300, which we will go through in the following section.
Discussion
This study represents the first of its kind in the western region of Saudi Arabia since it seeks to investigate the perception and influence of AI on the educational experiences of medical students and interns. There is a limited amount of literature available nationally as well as globally about AI in medical education. Thus, our study will add significant impact on medical education journey.
The findings of our study found that most medical colleges in Western region did not receive any education regarding the use of AI with 72%, despite this the majority of participants 92.3% were familiar with the concept of AI, and that 92% of both the preclinical and clinical years students had the same level of awareness concerning AI. A study conducted by Wajid Syed explored the awareness of pharmacy students in Riyadh about artificial intelligence. Most of the students (n = 116; 73.9%) were aware. 12 Conversely, a study on dental students in Saudi Arabia concluded that only 44.2% were aware of the usage of AI in dentistry. 18 In a study conducted on 332 medical students from different universities in the eastern region of Saudi Arabia. It indicates that the majority of participants, 87.12%, believe that artificial intelligence will play an important role in healthcare. 19 Another study conducted in Western Australia involving 134 medical students revealed that the majority (87.5%) had not received any AI training. However, 58.6% of them believed that AI should be included in medical education, and 72.7% expressed a desire for more AI-focused teaching. 20 Furthermore, a mixed-method cross-sectional study in Palestine explored undergraduate medical students’ perceptions of AI in medicine. Among 349 students surveyed and 15 interviewed, the majority (76.8%) had no formal AI education, and 74.5% lacked AI training in their studies. Despite this, 67.9% believed AI would revolutionize medicine, and 70.8% emphasized the importance of integrating AI training into medical curricula. 21
Although they did gain exposure to AI mainly from media (TV, YouTube, Twitter) followed by family and friends. Most of respondents agreed that the most useful way to explore the AI in medicine was Q&A panels with experts and short lectures.
Depending on the experience of the participants with AI, 67.2% were agreed and strongly agreed that AI positively impact their education, in contrast 19.2% believe that AI can negatively impact their education. Additionally, 65.8% agree and strongly agree that the training on AI concepts during medical school can be helpful in the educational journey, aligns with the finding of Alwadani FAS et al study, where they found that only 26.7% believed that learning AI will significantly detract them from medical school curriculum. 19 Another study conducted by Sami A. Alghamdi and Yazeed Alashban on 1212 student from many universities all around Saudi Arabia found that 69.5% of students (n = 842) supported the statement “All medical students should receive teaching in artificial intelligence,” and only 12.7% (n = 154) disagree. 22
On the other hand, 42.9% were agreed and strongly agreed that using AI for study purposes will strengthen the knowledge needed to work with AI in routine clinical practice. These findings demonstrate that the majority of medical students and interns in western regions like many other regions have positive views on the experience of AI. However, 5.8% express their concerns about ethical implications of using AI in medical education.
There were no significant changes in the opinions of participants toward AI according to age, gender, or educational year. These findings align with a study conducted in India. 23 This implies that participants’ opinions toward AI in medical education may not be significantly impacted by these demographic factors, or educational year. However, our findings show that medical students’ perceptions of AI may be influenced by institutional variables. Across several universities and hospitals, a study evaluated medical students’ acceptability and intention to use medical AI using the Unified Theory of Acceptability and Use of Technology model. According to the study, students from different universities may have various views about AI education due to variations in curriculum design, exposure to AI technology, and institutional emphasis on AI education. 24 These findings suggest that colleges that prioritize integrating AI into their medical curricula may encourage more positive opinions among students.
Our study highlighted the most beneficial aspects of AI integration in medical education were beneficial, getting help with paraphrasing and grammar checking, followed by summarizing medical literature, then in research assistance
Using AI driven platforms can enhance the medical experience by several methods according to participants opinions, where they can select more than one method, 27% of them choose that the AI can help them spend less time obtaining information, which is the highest, in chronological order the rest methods will be listed. AI allows them to access knowledge from various sources effortlessly, makes learning more enjoyable, helps them develop learning tools that aid their progress and improve their grades.
Conversely, the following aspects of AI used should be addressed to utilities the AI in the most effective way. Firstly, AI has a potential for errors and bias with 23.6%. Secondly, it may oversimplify complex subject leading to superficial understanding. Thirdly, can lead to overreliance and a loss of critical thinking skills, problem-solving abilities, then, AI may provide inaccurate and biased information, lastly, AI could reduce interpersonal and communication skills. This research indicates that more than half (57.0%) of medical students and interns surveyed anticipate that AI will play a significant role in routine clinical practice in the future. This aligns with the findings of a study conducted in Oman, where students expressed confidence in AI's ability to enhance healthcare and support healthcare professionals in various responsibilities. 25 In another study of Malaysian medical students, 87.36% agreed that AI will play an essential role in healthcare. 26 In a study conducted on Jordanian medical students found that 78.3% (N = 191) expressed concerns about ChatGPT's potential inaccuracies, with accuracy and reliability cited as primary concerns. 27 According to our participants, radiology is predicted to be the most affected specialty, with 10% selecting it. A study by Ahmad A. et al supports this, showing that radiology residents believe AI will have a high impact on radiology workflow, with more than 80% seeing a positive impact on the technical and management aspects. 28
The participants have provided various suggestions to enhance the deployment of AI in order to better cater to the needs of medical students and interns. To obtain higher education from a college by instructing the participants on its usage and how to handle it, in order to provide them with complimentary access to some artificial intelligence tools and offer optional lectures on their responsible utilization, and rectify errors in research studies, offer concise elucidations for certain subjects. To enhance the application of AI in medical education, we can enhance the quality of programs to minimize biases and errors. Finally, we can educate students on the appropriate and ethical utilization of AI.
Certain limitations may have affected the research's conclusions. In this study we depended on online survey thus we relayed on the participants to accurately answering the questions without the ability to verify them which may have contributed to a potential bias. Additionally, although the questionnaire was distributed randomly to medical students, participation was voluntary, which may introduce a degree of self-selection bias. Students with a stronger interest in Al may have been more likely to respond, potentially leading to an overestimation of Al awareness and positive perceptions. Future studies could benefit from employing stratified random sampling to ensure a more representative sample of the student population. Our study was conducted only in the western regions of Saudi Arabia. For that, we suggest generalizing this study on all the regions of Saudi Arabia, and instead of being on only medical students, we suggest applying t in the others educational fields.
We faced several challenges while conducting this study, we needed to send several reminders to motivate our targeted participants to complete the questionnaire. Furthermore, it was difficult to reach enough participants from the private medical colleges.
Conclusion
In conclusion, this study proved that medical students and interns in the western regions of Saudi Arabia are aware of the useful impacts of AI in their education. To maximize the benefits of AI in medical education, universities should consider implementing AI literacy programs as our research found that the majority of the participants did not receive any formal education, embedding AI into problem-based learning curricula, and providing targeted training based on students’ needs as a notable portion of our responders beliefs in advantages of integrating AI training into their medical education. Addressing AI-related ethical concerns should also be an integral part of medical education reforms, thereby minimizing ethical conflicts regarding AI usage. AI will undoubtedly play a significant role in the development of the medical education field. To harness the potential of AI effectively, we also recommend aligning with global and local health policy benchmarks, such as those set by the World Health Organization (WHO) or the Saudi Vision 2030 framework, which emphasizes digital transformation in healthcare and education.
Acknowledgment
We would like to thank our university for facilitating the ethical approval process and we want to express our sincere gratitude to our supervisor for her invaluable guidance and support throughout this research project. Her expertise, encouragement, and insightful feedback have been instrumental in shaping the success of this endeavor. We are grateful to our families, whose unwavering love and support have been a constant source of motivation and strength during this journey.
Footnotes
ORCID iDs: Duaa S. Alkhayat https://orcid.org/0009-0007-1123-0169
Hind N. Alsubaiyi https://orcid.org/0000-0002-1303-9268
Yara A. Alharbi https://orcid.org/0000-0002-9132-9308
Lina M. Alkahtani https://orcid.org0000-0002-8361-5961
Afnan M. Akhwan https://orcid.org/0000-0002-8077-5679
Alhanouf A.Alharbi https://orcid.org/0000-0003-1784-5328
Informed Consent: Informed consent was obtained from all subjects involved in the study.
Author Contributions: H. Alsubaiyi, A. Alharbi and A. Akhwan contributed to conception and designing the study; Y. Alharbi, and L. Alkahtani contributed to statistical analysis and interpretation of results; D. Alkhayat contributed to critical revisions and supervision. All authors participated in data collection, writing the study manuscript, reviewed and approved the final manuscript.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement: The data will be available from the correspondence author upon reasonable request to protect the participants confidentiality.
Institutional Review Board: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Taibah university college of medicine (Reference Number: TU-24-08) Madinah, Saudi Arabia.
References
- 1.Sallam M. Practice : systematic review on the promising perspectives and valid concerns. Healthcare. 2023;11(6):887. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Chen M, Zhang B, Cai Z, et al. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front Med. 2022;9. doi: 10.3389/fmed.2022.990604 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Buch VH, Ahmed I, Maruthappu M. Debate & analysis artificial intelligence in medicine. Br J Gen Pract. 2018;68(March):2016–2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Al Saad MM, Shehadeh A, Alanazi S, et al. Medical students’ knowledge and attitude towards artificial intelligence: an online survey. Open Public Health J. 2022;15(1):1–8. doi: 10.2174/18749445-v15-e2203290 [DOI] [Google Scholar]
- 5.Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253–e261. doi: 10.1016/S1470-2045(19)30154-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–175. doi: 10.1136/bjophthalmol-2018-313173 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT-Reshaping medical education and clinical management. Pakistan J Med Sci. 2023;39(2):605–607. doi: 10.12669/pjms.39.2.7653 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Wang F, Preininger A. AI In health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(1):16–26. doi: 10.1055/s-0039-1677908 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22(1):1–9. doi: 10.1186/s12909-022-03852-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7 [DOI] [PubMed] [Google Scholar]
- 11.Matheny M, Israni ST, Ahmed M. Artificial intelligence in health care. Natl Acad Med. Published online 2018:1–269. [Google Scholar]
- 12.Syed W, Basil A, Al-Rawi M. Assessment of awareness, perceptions, and opinions towards Artificial Intelligence among healthcare students in Riyadh, Saudi Arabia. Med. 2023;59(5). doi: 10.3390/medicina59050828 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Liu DS, Sawyer J, Luna A, et al. Perceptions of US medical students on Artificial Intelligence in medicine: Mixed methods survey study. JMIR Med Educ. 2022;8(4). doi: 10.2196/38325 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Buabbas AJ, Miskin B, Alnaqi AA, et al. Investigating Students’ perceptions towards Artificial Intelligence in medical education. Healthc. 2023;11(9):1–16. doi: 10.3390/healthcare11091298 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Sit C, Srinivasan R, Amlani A, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11(1):7–12. doi: 10.1186/s13244-019-0830-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Choi H, Sunwoo L, Cho SJ, et al. A nationwide web-based survey of neuroradiologists’ perceptions of artificial intelligence software for neuro-applications in Korea. Korean J Radiol. 2023;24(5):454–464. doi: 10.3348/kjr.2022.0905 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Sharma A, Minh Duc NT, Luu Lam Thang T, et al. A consensus-based Checklist for Reporting of Survey Studies (CROSS). J Gen Intern Med. 2021;36(10):3179–3187. doi: 10.1007/s11606-021-06737-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Khanagar S, Alkathiri M, Alhamlan R, Alyami K, Alhejazi M, Alghamdi A. Knowledge, attitudes, and perceptions of dental students towards artificial intelligence in Riyadh, Saudi Arabia. Med Sci. 2021;25(114):1857–1867. [Google Scholar]
- 19.Lone A. Attitude and understanding of Artificial Intelligence among Saudi medical students: an online cross-sectional study [Response to Letter]. J Multidiscip Healthc. 2024;17(April):2561–2562. doi: 10.2147/JMDH.S478611 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Stewart J, Lu J, Gahungu N, et al. Western Australian medical students’ attitudes towards artificial intelligence in healthcare. PLoS One. 2023;18(8 August):1–14. doi: 10.1371/journal.pone.0290642 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Jebreen K, Radwan E, Kammoun-Rebai W, et al. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC Med Educ. 2024;24(1):1–19. doi: 10.1186/s12909-024-05465-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Alghamdi SA, Alashban Y. Medical science students’ attitudes and perceptions of artificial intelligence in healthcare: a national study conducted in Saudi Arabia. J Radiat Res Appl Sci. 2024;17(1):100815. doi: 10.1016/j.jrras.2023.100815 [DOI] [Google Scholar]
- 23.Jackson P, Ponath Sukumaran G, Babu C, et al. Artificial intelligence in medical education - perception among medical students. BMC Med Educ. 2024;24(1):1–9. doi: 10.1186/s12909-024-05760-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Li Q, Qin Y. AI In medical education: medical student perception, curriculum recommendations and design suggestions. BMC Med Educ. 2023;23(1):1–8. doi: 10.1186/s12909-023-04700-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H. Knowledge, attitudes, and perceptions of artificial intelligence in healthcare among medical students at Sultan Qaboos University. Cureus. 2023;15(9). doi: 10.7759/cureus.44887 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Tung AYZ, Dong LW. Malaysian medical students’ Attitudes and readiness toward AI (Artificial Intelligence): A cross-sectional study. J Med Educ Curric Dev. 2023;10. doi: 10.1177/23821205231201164 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Hammour A A, Hammour KA, Alhamad H, et al. Exploring Jordanian medical students’ perceptions and concerns about ChatGPT in medical education: a cross-sectional study. J Pharm Policy Pract. 2024;17(1):1–17. doi: 10.1080/20523211.2024.2429000 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Mirza AA, Wazgar OM, Almaghrabi AA, et al. The use of artificial intelligence in medical imaging: a nationwide pilot survey of trainees in Saudi Arabia. Clin Pract. 2022;12(6):852–866. doi: 10.3390/clinpract12060090 [DOI] [PMC free article] [PubMed] [Google Scholar]