The study was conducted to assess the level of readiness for artificial intelligence among medical and dental professionals, particularly in Peshawar, using a validated MAIR-MS tool. Their readiness was assessed using the aggregate scores attained by the students on the validated tool, which encompassed four domains: cognitive, ability, vision, and ethics. A higher score signified greater agreement with the survey questionnaire statements and an elevated level of readiness toward AI among undergraduate medical and dental students. The primary purpose of this study was to determine the readiness of upcoming healthcare professionals for AI, which would help us devise possible solutions for implementing this new technology in daily life.
This research demonstrated that Pakistan’s medical and dental students (97.3%) exhibited moderate readiness for AI (62.2%), with lesser levels at 29.7% and higher levels at 8.1%. Male students exhibited a significantly higher level of AI readiness than their female counterparts. No significant differences were observed between the medical and dental disciplines or across different academic years. Numerous studies have repeatedly identified disparities in AI readiness between genders. It is suggested that male students typically exhibit more outstanding expertise and excitement for AI than their counterparts. Our research findings yielded identical outcomes.
Various variables may contribute to these gender discrepancies, including societal expectations, educational backgrounds, and access to technology. Societal preconceptions and expectations can impede girls and women from pursuing jobs in technology and science, leading to diminished exposure to AI and other disciplines. Additionally, male and female students may have different educational experiences and opportunities to learn about AI, with males potentially having better access to relevant courses, resources, and mentorship. Cultural variables and gender norms can influence attitudes toward innovation and technology. Women may be discouraged from engaging in AI due to perceptions that associate technology with masculinity [17], and their access to education and employment possibilities in the technology sector may be limited by societal norms that prioritize traditional gender roles.
A study conducted in Beijing in 2020 by Yun Dai et al. revealed that male students reported higher relevance, confidence, and readiness for AI compared to female students [18]. In a study conducted by Dos Santos et al., male participants exhibited greater confidence in utilizing AI applications and demonstrated reduced apprehension toward AI technologies [14]. A notable distinction regarding gender and the integration of AI into clinical practice was identified in another investigation examining physician perspectives on AI’s role in diagnostic pathology conducted by Sarwar in Canada. The study found that males exhibited greater comfort in working with computer science technology compared to females [19]. These findings reinforce our study’s results, emphasizing the need for interventions to address gender disparities in AI education and technology adoption.
Contrary to our findings, a study conducted in Malaysia showed that gender was not significantly associated with AI readiness [20]. Another cross-sectional survey showed no gender differences in student’s AI literacy [18]. The Medical Artificial Intelligence Readiness Scale (MAIRS) did not reveal any substantial disparities in AI readiness between male and female students in a study by Halat et al. [21]
There have been mixed findings in studies about the main differences between dental and medical students when it comes to the readiness for artificial intelligence. While some research suggests that dentists may be better equipped to deal with AI, other studies have not shown any significant variations. The varying outcomes of the readiness of medical and dental students for AI could originate from multiple causes. Various study designs, sample sizes, and data collection techniques can influence the outcomes. Moreover, several studies may have exclusively examined the readiness of the two groups for AI, whereas others may have pursued broader objectives. Differences in culture, education, and society between regions can also affect how ready healthcare students are for AI. This could be due to a similar curriculum regarding AI content, shared experiences, and equal exposure to AI. These things show that more study is needed to better understand how medical and dental professionals might be different in their readiness for AI.
Researchers have encountered inconsistent results while assessing the AI readiness of dental and medical students. In comparison to medical professionals, dental professionals in Saudi Arabia exhibited a higher degree of readiness for AI integration [22]. Our findings are consistent with the research, which indicates that dental students are more prepared for AI. Conversely, medical students are more moderately prepared for AI. This may be a consequence of the dental profession’s growing dependence on machinery and technical expertise in practice. Dentists have adopted recent technological advancements, including 3D printing, scanning, and digital radiographs [23,24,25,26]. Another study conducted in Malaysia matches our findings, which suggest intermediate preparation for medical students for artificial intelligence [20]. The absence of satisfactory readiness indicates that medical and dental professionals lack the necessary knowledge and preparedness to implement AI. The disparity must be addressed, as it is essential in light of the transformative impact of AI on the future of healthcare. This will require substantial effort [2, 27]. In contrast, dental students exhibit greater preparedness compared to their counterparts regarding AI readiness scores. According to another researcher, a comparison of the two groups revealed no statistically significant differences [28]. Numasawa’s research indicates that dental students are less equipped for interdisciplinary learning than their colleagues in medicine and nursing [29].
The literature on AI readiness among medical students indicates that various factors can influence their readiness for AI integration in healthcare. These criteria include the year of study, prior AI experience, and the curriculum’s emphasis on AI-related topics. Various elements determine AI readiness and the year of study. Prior AI training and an emphasis on AI-related themes in medical curriculum are all important considerations. Furthermore, curriculum consistency across years may help students achieve a more uniform degree of AI preparation. As students continue through their medical education, their AI skills may gradually improve. Furthermore, shared experiences and exposure to AI-related concepts throughout the academic curriculum may have contributed to students’ common understanding and preparation for AI in our study.
A study carried out in Malaysia established a significant association between study years and AI readiness in medical students, specifically [20]. However, our study did not show any significant difference between the academic year and the AI readiness score. A prior investigation revealed that students expressed concerns regarding the insufficiency of their medical education in equipping them to collaborate effectively with AI tools or applications. The consensus was that additional preparation within the medical program was essential to enhance their AI readiness level [30].
Studies on AI readiness indicate notable differences among countries. An index assessing AI capabilities and readiness across 80 countries indicates that the USA, China, Japan, and South Korea are at the forefront. In contrast, numerous countries in Africa, Asia, and Latin America are significantly behind [31]. In developing countries such as Palestine, the low awareness of AI and its limited application across various sectors underscore the necessity for focused national strategies [32]. In advanced economies such as Saudi Arabia, healthcare professionals exhibit low levels of AI readiness, indicating a necessity for enhanced education and training [22].
This study revealed that Pakistani and non-Pakistani students showed comparable results, which were statistically insignificant. The reason behind this could be the similarities in the students’ experiences and the influence of shared aspects with a lack of diversity from individuals outside Pakistan. It could limit the capacity to identify meaningful variations among nationalities. The limited number of students from outside Pakistan may introduce bias and necessitate additional research in the future to thoroughly evaluate their connection.
Readiness for Artificial Intelligence across sectors, especially in healthcare and education, involves four critical factors: cognition, ability, vision, and ethics [15, 33, 34]. These factors are essential for medical students and educators in their fields to effectively integrate AI. Research has established and validated instruments to assess AI readiness, including the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) [15] and comparable tools for educators [34].
It is necessary to take a holistic view of dental and medical students’ AI readiness by investigating their skills, ethics, vision, and cognitive processes. If students can develop a strong cognitive foundation in AI and demonstrate proficiency in using AI tools and applications, they are more prepared for the integration of AI. An in-depth understanding of ethical concerns, as well as the ability to visualize and evaluate artificial intelligence results, are equally vital. Addressing all four criteria can improve medical institutions’ ability to better equip students to use artificial intelligence to improve healthcare outcomes. Our study indicates a moderate level of readiness in both medical and dental students.
Limitations of the study
This study identifies several limitations that should be considered when interpreting the findings. The restricted sample size, particularly concerning non-Pakistani students, may limit the applicability of the findings. Relying on self-reported data can result in biases, such as social desirability bias. The study’s cross-sectional design limited the evaluation of changes in AI readiness over time. The identified limitations may impede the study’s ability to comprehensively assess AI readiness among medical and dental students in Peshawar.
Future research
Future research should consider several areas to solve the limitations of this study and provide a more thorough understanding of medical and dentistry students’ preparedness for AI. Expanding the research sample sizes, especially by incorporating more non-Pakistani students, can improve the findings’ representativeness. A more excellent knowledge of AI readiness characteristics and their influence can be obtained using mixed methods research, combining quantitative and qualitative methodologies. Studies that follow participants’ improvements in AI readiness over time can evaluate the efficacy of interventions and spot new patterns. Furthermore, contrasting the degree of AI preparation among students in various parts of Pakistan and with those in other nations might shed light on regional differences and international best practices.
Recommendations
The results of this research have implications for decision-makers and educational organizations in Pakistan for a focused effort to prepare dental and medical students for the integration of AI technologies into their curriculum. Providing adequate training and resources for faculty members to practically engage students with AI tools while focusing on ethical aspects, promoting gender equality, and fostering an innovation culture through national programs. The suggestions are intended to allow healthcare workers to effectively use AI to improve care and healthcare results in Pakistan.
