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

Abstract

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12909-024-06446-3.

Background

Artificial intelligence (AI) is a rapidly emerging field with the potential to affect clinical practice. This technology involves the development of algorithms or models that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and language translation [1]. AI is powered by advances in machine learning and deep learning, which allow algorithms to progressively improve their performance on such tasks through exposure to large amounts of data [2].

AI can advance clinical care and efficiency in areas such as diagnostics, treatment planning, and resource allocation, but individual readiness remains a major barrier to adoption [3]. For example, AI-powered algorithms can produce diagnoses at the same level of accuracy as board-certified dermatologists [4], and although the use of AI is not yet widespread, these results emphasize the potential for exponential growth and adoption.

The rapid expansion of AI within specialties such as radiology and pathology requires foundations in medical education to allow clinicians to adapt their expertise and careers to these emerging technologies [5]. Already, the perception of AI by trainees is shaping specialty choice, especially in the aforementioned specialties [6]. Individual readiness to adopt AI is influenced by awareness, beliefs, and personal factors such as innovation and incentives [3]. These factors interact with organizational and educational readiness. Integrating AI discussion into medical education curricula is critical to ensure that future physicians are adequately prepared to assess these technologies.

Several studies worldwide have investigated medical trainees’ knowledge and perceptions of AI [7–14]. However, few have comprehensively explored their knowledge of and information sources used regarding the use of AI in clinical practice across the years of their education. One particular gap is the examination of various student years, as well as the links between student interest, knowledge, and specialties. Examining such perceptions during various education phases can facilitate the development of strategies to promote the systematic introduction of AI topics [15, 16].

This study evaluated medical students’ current AI knowledge, exposure and information sources, views on how AI may influence different specialties, and understanding of its evolving role in the field. The findings can help guide efforts to reform medical education curricula to maintain student engagement that aligns with future practice and prepares future physicians to effectively integrate AI.

Methods and materials

Study area and population

This cross-sectional study was conducted across various medical schools in Saudi Arabia. Most of the sample (n = 234, 66.1%) was from King Abdulaziz University, Jeddah. Hence, a comparison between schools was not possible because of the small number of participants from the remaining institutions. The remaining students were recruited from Batterjee Medical College (n = 21, 5.9%), Fakeeh College of Medical Sciences (n = 17, 4.8%), Ibn Sina National College for Medical Studies (n = 12, 3.4%), King Saud bin Abdulaziz University for Health Sciences (n = 56, 15.3%) and the University of Jeddah (n = 14, 4.0%). Data were collected during the 2023–2024 academic term. Individuals were eligible to participate in this research if they were medical students above 18 years of age.

Ethical approval

Ethical approval for the study was granted by the Institutional Review Board of King Abdulaziz University (Reference No. 500–23). Participation in this research was voluntary, and informed consent was obtained from all participants before data collection. To ensure confidentiality and anonymity, all information collected from participants was deidentified and stored securely.

Data collection tools

Data were collected via a previously validated survey delivered online via Google Forms [7]. Participants were recruited via convenience sampling by posting recruitment materials without a targeted outreach list. As such, no formal response rate could be calculated.

The first part of the questionnaire collected participants’ demographic details, such as date of birth, sex, year of study, and academic grade point average (GPA). The second part included questions regarding students’ previous exposure to AI, including the number of related courses they had taken and their self-rated interest in AI; participants answered these questions on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree.”

To assess students’ perceptions and understanding of AI, a previously validated survey focusing on the perceived impacts of the use of AI in medicine among medical students was employed in this research [7]. Participants also ranked their top three choices of medical specialties in each category. The survey collected participants’ opinions on how AI may influence specific medical specialties and career choices.

Finally, the students provided their views and preferences concerning AI education, including useful topics, delivery methods, and desired time commitments. This approach allowed us to explore the gaps between current and ideal AI learning opportunities.

Statistical analysis and data processing

Participants were divided based on whether they were students in their basic years of medical school (years 2–3) or their clinical years (years 4–6 and interns). All the questions scored on Likert scales were categorized as indicating either agreement (“agree” or “strongly agree”) or disagreement (“neutral,” “disagree,” or “strongly disagree”) to facilitate comparison between the groups.

Descriptive statistics included the means and standard deviations of continuous variables and the frequencies and proportions of categorical variables. Survey responses (agreement or disagreement) were compared between the groups (students in their basic years vs. clinical years) via the chi-square test. Associations were tested for continuous variables (the number of AI courses taken and the amount of time willing to spend learning about AI) via linear multiple regression modeling, whereas those pertaining to categorical variables (self-reported interest in AI) were tested via logistic multiple regression. The students’ age and sex were included as control variables in both cases. Multicollinearity was tested by calculating the variance inflation factor and was found to be negligible in all models. The linear regression model residuals were confirmed via the Kolmogorov–Smirnov test to be normally distributed. Statistical analyses were performed using R version 4.3.1. All significance tests used a threshold of α = 0.05.

Results

Sample description and interest in and exposure to AI in health care

The sample consisted of 354 medical students (63.8% male) whose mean age was 21.4 years (SD = 1.8). The sample characteristics are presented in Table 1. Most students (77.1%) had never taken a course in AI, but a similar proportion of students reported at least some level of interest in the field (64.7%). GPA was not associated with either AI exposure or interest. Students in different cohorts did not differ significantly in the number of AI courses they had taken or their level of interest in AI. As expected, students who reported having received formal AI training in their education also reported having taken more AI courses (t (350) = 11.18, p < 0.001) and a higher level of interest in AI (t (350) = 2.29, p = 0.022). Students in their clinical years exhibited significantly lower GPA values compared to those in their basic years (t (352) = 2.76, p = 0.006; Table 1).

Table 1.

Sample demographics and characteristics overall as well as by year of study, level of interest, and AI exposure

	Overall
Sample size	354
Sex (Male)—n (%)	226 (63.8)
Age—Mean (SD)	21.37 (1.75)
GPA—Mean (SD)	4.44 (0.46)
Number of AI courses—n (%)
None	273 (77.1)
One	48 (13.6)
Two	18 (5.1)
Three or more	15 (4.2)
Year of training—n (%)
2nd	60 (16.9)
3rd	51 (14.4)
4th	77 (21.8)
5th	130 (36.7)
6th	15 (4.2)
Intern	21 (5.9)
Interested in AI—n (%)
Strongly disagree	12 (3.4)
Disagree	16 (4.5)
Neutral	97 (27.4)
Agree	137 (38.7)
Strongly agree	92 (26.0)
Formal AI education—n (%)	76 (21.5)

When the participants were asked where they had been exposed to AI, the majority (78.2%) named public media (e.g., television, YouTube, and Twitter). Fewer than half reported exposure through family and friends (41.2%), followed by online sources (24.3%). Some students reported exposure through research projects (12.4%), peer-reviewed articles (10.2%), books (9.9%), lectures (9.0%), or conferences (4.5%).

Knowledge and perceptions of AI in medicine

The students’ responses to the questions concerning their understanding of AI are presented in Fig. 1. Students in their basic years of education were significantly more likely to report being able to list the advantages and benefits of AI in medicine (57.7%) than those in their clinical years (45.3%, χ² = 4.69, p = 0.030). Beyond these questions, no significant differences based on the year of education were observed.

Fig. 1.

Overall responses to the questions regarding AI

Most students felt that AI would play a significant role in medicine during their lifetime (65.8%) and were excited about the possibility of using AI technology as future physicians (59.0%). However, few understood fundamental AI concepts (e.g., cross-validation, 18.4%), could list examples of clinically relevant AI research (25.1%), or felt that their school offered resources to explore AI in medicine (20.1%).

Influence of information sources on knowledge

The participants reported that they had learned about AI concepts primarily from online forums (n = 86), books (n = 35), lectures (n = 32), media platforms such as Twitter or YouTube (n = 277), family and friends (n = 146), peer-reviewed journal articles (n = 36), professors and doctors (n = 49), and research projects (n = 44). Because of the small sample size, we did not consider students who reported that they had learned from conferences (n = 16).

We used linear regression to test the association between the number of AI courses and information sources. Participants who learned from online forums (t(345) = 2.05, p = 0.041), books (t(345) = 3.12, p = 0.002), or lectures (t(345) = 2.44, p = 0.015) had taken significantly more AI courses than students who reported learning from friends and family (t(345) = − 3.61, p < 0.001) or media platforms (t(345) = − 3.78, p < 0.001).

We tested the participants’ levels of agreement (“agree” or “strongly agree”) with each survey question via logistic regression. Each answer was tested for associations with self-reported use of all information sources. Students who relied on media were more likely to indicate that AI would play a significant role in medicine in their lifetime (OR = 2.76, Z = 3.74, p < 0.001), they could list the advantages and benefits of the use of AI in medicine (OR = 1.95, Z = 2.43, p = 0.015), training in these concepts would be useful in their careers (OR = 2.27, Z = 3.01, p = 0.003), and they wanted to learn what medical students should know regarding AI in medicine (OR = 1.89, Z = 2.38, p = 0.017).

Participants who received information from family and friends were significantly more likely than others to be excited about using AI as future physicians (OR = 1.81, Z = 2.56, p = 0.010), as were students who learned from research projects (OR = 2.47, Z = 2.16, p = 0.031). Participants who relied on books were more likely than others to report that they understood AI concepts, such as convolutional neural networks and cross-validation (OR = 5.94, Z = 4.13, p < 0.001), be able to separate “hype” from clinically relevant AI articles (OR = 2.38, Z = 2.13, p = 0.033), and report that their school offered resources to explore AI in medicine (OR = 2.35, Z = 2.06, p = 0.040). Students who learned from professors or doctors reported that their school provided resources to support exploration at similar levels (OR = 2.29, Z = 2.31, p = 0.021).

Students who reported higher levels of interest in AI tended to respond more positively to survey items focused on AI knowledge and perceptions than those with lower levels of interest. Students who exhibited higher levels of interest were more excited to use AI technology as future physicians (Q2, r = 0.154), reported that they understood AI concepts (Q3, r = 0.153), indicated that they could list recent examples of clinically relevant research (Q4, r = 0.157) and the advantages of AI in medicine (Q5, r = 0.106), believed that training in AI concepts would be helpful (Q10, r = 0.110), and wanted to learn more about AI in medicine (Q13, r = 0.145). Notably, although all these correlations were significant, they were generally weak (range: 0.106 to 0.157).

Students who had taken more AI courses were significantly less likely than those who had taken fewer courses to believe that AI would play a significant role in medicine during their lifetime (Q1, r = -0.193), be excited about using AI technology as future physicians (Q2, r = -0.155), view training in AI concepts as useful for their future careers (Q10, r = -0.162), and want to learn more about AI in medicine (Q13, r = -0.142). However, these students reported that they could list recent examples of clinically relevant AI research (Q4, r = 0.109). Again, all the correlations were generally weak (range: 0.109 to 0.193).

What students want from AI education

On average, students reported that they were willing to spend 1.89 h per month learning about these topics (SD = 1.43). Students who reported higher levels of interest in AI were significantly more willing than those with lower interest to spend more time learning about these topics, after adjusting for age and sex (t (349) = 3.40, p < 0.001). However, the reverse was true of students who had taken more AI courses, who were less willing to spend time learning (t (349) = -2.36, p = 0.019); this may be because they had already learned about this topic. When participants were asked about the most useful ways in which their school could promote AI exposure among students, approximately half mentioned short lectures on the fundamentals of AI in medicine (51.9%), question-and-answer panels with leaders in the field (46.3%), and workshops on programming AI models (41.2%).

When participants were asked about the subjects that would be most interesting to explore, the majority mentioned the topics of when to use AI in medicine (62.9%) and its strengths and weaknesses (51.7%). Other topics of interest included AI ethics (43.8%) and the use of AI in medical research (40.1%). Relatively few expressed interest in concrete elements of AI implementation and use, such as the roles played by individuals on multidisciplinary teams researching AI (10.4%), ways of critiquing AI models (11.0%), and the process of developing AI models (18.9%).

Perceived influence of AI on medical specialties

Only a minority of students (22.3%) reported that they were less likely to work in specialties in which AI was perceived to have the most influence. The majority indicated that their specialty choice would not be affected by this consideration. When participants were asked about their intended field of practice, students in their basic years were most likely to indicate general surgery (30.6%), dermatology (29.7%), neurosurgery (21.6%), internal medicine (20.7%), and emergency medicine (20.7%). Students in their clinical years were most likely to indicate internal medicine (37.0%), followed by family medicine (25.9%), general surgery (24.7%), emergency medicine (19.3%), and ophthalmology (18.9%).

When participants were asked about the fields that they believed would be affected most strongly by AI, students in their basic years of education named diagnostic radiology (34.2%), general surgery (30.6%), pathology (24.3%), anesthesiology (23.4%), and family medicine (19.8%). Students in their clinical years shared similar opinions but at considerably higher rates, citing a higher likelihood of impacts on radiology (52.7%), pathology (34.6%), and family medicine (28.4%). Students in their clinical years were less likely to expect impacts on general surgery (20.6%) or anesthesiology (20.6%). Both groups reported that the impacts of AI were least likely to be observed in otolaryngology (0.9% basic, 1.2% clinical), urology (3.6% basic, 2.1% clinical), and obstetrics and gynecology (0.9% basic, 3.3% clinical).

Discussion

Increasing interest has examined AI’s future role in medicine [17], but the incorporation of formal AI education into medical curricula has been inadequate. Previous researchers have called for further research in this area [18–20].

Student interest in, knowledge of, and exposure to AI in health care

The participants reported that AI would play a significant role in the future of health care, as has been reported in previous research at the global level [12]. However, the majority of students in our sample reported that they had not taken any formal AI education courses. The contrast between students’ beliefs regarding the future role of AI in health care and their limited formal education in this regard is not surprising and has also been reported elsewhere [12, 13, 21, 22]. Notably, although students generally reported that AI would have a significant impact on medicine, their self-reported understanding of core concepts and applications in this context was limited. Only a small proportion reported a strong understanding of fundamental AI concepts or were aware of clinically relevant AI research. Although we did not objectively assess knowledge, the fact that few students even claimed this understanding suggests a lack of technical education in AI methodology. The proportion of students reporting a strong understanding was lower than some but not all previous reports [7, 10, 22, 23], which may be in part due to geographic variation.

The primary source of AI exposure for most students was informal channels, such as traditional and social media, rather than formal educational resources. This suggests that students pursue their interest in AI in medicine via these informal sources. These observations held for both students in their basic years and those in their clinical years and have also been reported in similar work [9]. Such reliance on unofficial sources likely contributes to either the observed gaps in students’ fundamental understanding of AI concepts or their lack of self-reporting of that knowledge. This conclusion is further evidenced by the result that students who relied on more scientific sources, such as books and articles, exhibited more foundational AI knowledge than did students who relied on informal sources, as has previously been reported [24]. This suggests that social media strongly influences the knowledge of medical students [25].

Students in their basic years reported being more likely to be able to identify the benefits of AI in medicine than did students in their clinical years. This decrease in knowledge could be because as they progress through their clinical years, students tend to focus more on their studies rather than pursuing extracurricular topics, including AI in medicine, due to their fear of decreased academic performance [26]. Additionally, AI is evolving so rapidly that even a 1–2 year difference in age results in different outlooks; if the curriculum does not provide a strong academic foundation concerning AI, students cannot obtain a solid grasp of the fundamental concepts, making keeping pace with advances in the field difficult [27].

Interestingly, a discrepancy was observed between students who were interested in AI and those who were exposed to AI. Students who were interested in AI believed that it would play a greater role in medicine in the future, reported a greater understanding of AI concepts in medicine, and were eager to obtain more training and learning opportunities. Academic interest is a strong driver of further learning and development, particularly in medical disciplines [28]. Stimulating students’ interest in their education is crucial to improving their learning experience and academic performance [29].

Students who had taken more AI courses were, in contrast, less willing to devote additional time to learning about AI. This may indicate that students who have previously been exposed to AI in medicine may reach a point at which their extensive prior education leads them to adopt a more nuanced and potentially skeptical perspective on its role. Previous research has revealed that medical students who exhibit strong foundational knowledge of AI are concerned regarding its integration into medicine, including by highlighting issues related to AI integrity [23]. Notably, students were more likely to report the need for fundamental knowledge of the role of AI in medicine—such as ethics, uses, and advantages and weaknesses—than technical details [7].

AI and specialty choice

The majority of students did not believe that their future career in a particular specialty would be impacted by AI. Although students in their basic years agreed that surgery would be the second most strongly affected field, they also indicated that this specialty represented their top preference. Similarly, students in their clinical years identified family medicine as a top specialty in terms of both being impacted by AI (third) and their specialty preferences (second). These findings may indicate that students do not necessarily believe that the integration of AI into medicine will reduce physicians’ importance in the field, which has also been reflected by research in other contexts [22, 30].

Notably, aligning with previous findings [22, 30], radiology was identified as the top specialty in terms of the impact of the integration of AI into medicine. A higher proportion of students in their clinical years (compared to those in their basic years) chose this option. This finding contrasts with those of a study conducted in the United States, according to which AI-related anxieties influenced students’ preferences, leading many to reconsider radiology as a career choice [6]. Pathology was identified as the second most likely specialty to be impacted by AI by students in their clinical years, whereas it was the third choice among students in their basic years. Surgery was ranked highly among the specialties likely to be affected by AI, reflecting ongoing efforts to incorporate AI into surgical procedures [31]. Nevertheless, this field was ranked highly as a career choice by students in both their basic and clinical years. However, students’ baseline interest was not garnered in our study, and their interests may have been in areas where they felt that AI would have little impact. Future work should fully address students’ anticipated impact of AI as well as their interest in all areas to better understand the cross-section of the two.

Limitations

This work provides an overview of students’ perspectives on the use of AI in medicine, including their choices regarding career specialties. The main limitations of the research pertain to its cross-sectional study design and that it was only done in Saudi Arabia. Its applicability is thus limited, despite the results being broadly similar to other studies in other countries. A more detailed, in-depth, and qualitative evaluation of students’ opinions may provide further insights into the gap between their enthusiasm and foundational knowledge as well as their career choices. Future work could also assess objective knowledge of AI to see whether the reported informal sources of information are providing a relevant technical understanding of AI methods. Addressing these limitations through broader, objective, longitudinal research with baseline and follow-up assessments can shed further light on the dynamic relationships among AI exposure, medical education, and students’ career choices.

Conclusion

Although positive attitudes among students to the use of AI in health care are crucial for the successful implementation of such technology, this research highlights the opportunity to enhance AI education. This gap may hinder students’ ability to take full advantage of AI technologies in their future clinical practice [32]. Addressing these disconnects through targeted curriculum reforms is imperative to prepare future physicians optimally to engage in AI-enabled health care. Bridging these gaps between interest and knowledge can allow the benefits of AI to be more effectively obtained. Key recommendations include the development of adaptable AI programs that evolve alongside technological advances, increasing the availability of high-quality educational resources, and longitudinally monitoring changing perspectives.

Abbreviations

AI

Artificial Intelligence

GPA

Grade Point Average

Authors’ contributions

AFA conceptualized and designed the study, supervised the project, performed data analysis and interpretation, and critically revised the manuscript. AFA, AA, FS, SK, MK, AM, MB,NF, MA, and MB were involved in the investigation, ethics application, resource collection, and writing of the original draft. All authors read and approved the final manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request afmalmarzouki@kau.edu.sa.

Declarations

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Research Ethics Committee (REC) of King Abdulaziz University (Reference No. 500–23). All participants provided informed consent before participating in the study, as outlined in the methodology. This research was conducted in accordance with the Saudi Law of Ethics of Research on Living Creatures and its Implementing Regulations as well as the guidelines provided by the National Committee of Bioethics (NCBE).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.