Introduction

Higher education systems, as dynamic subsystems of broader social structures, interact with and influence their surrounding social environments. To remain relevant and effective, these systems must adapt to technological advancements by integrating modern technologies and offering services that meet emerging social and individual needs ( 1 ). Artificial intelligence (AI) encompasses various capabilities, including natural language processing, expert systems, machine vision, speech recognition, and more. This technology can perform functions that, in many cases, surpass human activities in terms of accuracy and quality ( 2 ). In the field of medical education, the rapid advancement of technology continuously expands the scope of knowledge required for expertise in this domain. This ongoing expansion places a significant burden on professionals and creates an increasing demand for innovative approaches to learning and education ( 3 ). AI-based technologies play a crucial role in medicine by analyzing and processing vast amounts of information. Consequently, they contribute significantly to enhancing medical education processes ( 4 , 5 ). Given its high potential to revolutionize medical education, the use of AI in this field has become an inevitable reality ( 6 - 7 ). The utility of AI technologies, such as ChatGPT, in education and research is nuanced, offering both significant advantages and potential drawbacks, while posing unique challenges and opportunities ( 8 , 9 ).

This duality underscores the need for careful consideration as we navigate the integration of AI into medical education. Some of the relevant challenges include concerns over privacy, data confidentiality, lack of informed patient consent for using personal data, insufficient oversight in treatment decision-making, and potential risks to patient health ( 10 - 12 ). Conversely, the opportunities created by AI in medical education are noteworthy. These include decision support in diagnosis, workflow management, personalized learning, simulation-based education, and individualized feedback based on performance ( 13 - 15 ). Figure 1 provides an overview of the opportunities and challenges of AI in medical education.

Figure 1. Overview of the opportunities and challenges of artificial intelligence in medical education ( 12 ).

This study offers significant insights to medical educators and scholars by providing a comprehensive review of the opportunities and challenges presented by AI in medical education. While acknowledging the complexities of this evolving field, we primarily focus on the practical applications of AI within medical training. The distinctive aspect of this research lies in its use of the umbrella review method, which synthesizes the existing review research addressing AI in medical education. This approach provides deeper and more comprehensive findings regarding AI applications in medical education, thereby effectively addressing the gaps left by the previous individually conducted reviews.

Methods

The current study employed the umbrella review methodology to analyze the existing review literature and provide evidence on the applications of AI in medical education. An umbrella review synthesizes evidence from multiple reviews into a single comprehensive document, including systematic reviews that explore interventions or perspectives on a specific topic. This methodology is particularly recommended for addressing broad-scope issues where conflicting or divergent findings might arise.

To address the research objectives, the present umbrella review was performed to respond to the following question:

• - What are the applications of AI in medical education, based on the review studies conducted?

Search Methods

To gather relevant studies, databases including Science Direct, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar were systematically searched for the review studies published from 2018 to 2024.

Our search and screening method followed a three-step process. Initially, we created a list of search terms. In the next step, we conducted a preliminary search with keywords like ‘Artificial Intelligence AND Medical Education OR Systematic Review OR Scoping Review OR Meta-Analysis OR Narrative Review’. This resulted in developing the following keywords and combinations: ‘Systematic Review of Artificial Intelligence+ (OR # AND) Education’, ‘Systematic Review of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Scoping Review of Artificial Intelligence+ (OR # AND) Education’, ‘Scoping Review of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Meta-Analysis of Artificial Intelligence+ (OR # AND) Education’, ‘Meta-Analysis of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Narrative Review of Artificial Intelligence+ (OR # AND) Education’, ‘Narrative Review of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Rapid Review of Artificial Intelligence+ (OR # AND) Education’, ‘Rapid Review of Artificial Intelligence+(OR # AND) Medical Education’, ‘Critical Review of Artificial Intelligence+ (OR # AND) Education’, ‘Critical Review of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Theoretical Review of Artificial Intelligence+ (OR # AND) Education’, ‘Theoretical Review of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Historical Review of Artificial Intelligence+ (OR # AND) Education’, ‘Historical Review of Artificial Intelligence+ (OR # AND) Medical Education’, ‘Mapping Review of Artificial Intelligence+ (OR # AND) Education’, ‘Mapping Review of Artificial Intelligence+ (OR # AND) Medical Education’.

The literature search was guided by the following inclusion and exclusion criteria:

• • Inclusion Criteria
• - Relevance: Studies directly addressing the applications of AI in medical education
• - Review Type: Systematic reviews, meta-analyses, or other comprehensive reviews synthesizing primary research findings
• - Date Range: Articles published between 2018 and 2024
• - Language: Only English-language publications
• - Scope and Focus: Studies covering aspects relevant to AI applications in medical education
• • Exclusion Criteria
• - Irrelevance: Studies not directly related to the research question (i.e., AI applications in medical education)
• - Study Type: Research other than systematic reviews, meta-analyses, or comprehensive reviews, including informal reviews, conceptual articles, or case reports
• - Duplication: Studies already included in other reviews that do not provide new insights
• - Time Limitation: Articles outside the 2018-2024 publication range
• - Language: Non-English studies
• - Limited Scope: Studies examining specific AI aspects unrelated to medical education

Screening Process

Figure 2 illustrates the PRISMA flow chart detailing the literature search and screening process of the current umbrella review. The initial search of the databases led to the identification of a total of 362 studies. After screening the retrieved studies based on titles, abstracts, and keywords in accordance with the inclusion and exclusion criteria, a total of 77 reviews were deemed relevant.

Figure 2. Research screening process based on the PRISMA flow chart

Quality Appraisal

The identified articles were reviewed and evaluated by three of the authors of the study, using the Critical Appraisal Skills Program (CASP). In the event of any disagreement among the researchers regarding the criteria, the matter was thoroughly discussed to reach a consensus. If a unanimous agreement could not be achieved, the opinion with the majority vote was ultimately considered for the final decision. Each researcher conducted an independent review and filled out the evaluation checklists according to established quality criteria. Following this individual assessment, the team collectively compared and discussed the results. Any differences in scoring or interpretation were thoroughly analyzed, and agreement was achieved through discussion or, when needed, by seeking input from a third expert. This collective approach was utilized with the aim of enhancing reliability, minimizing bias, and strengthening the overall validity of the quality assessment process. Table 1 presents the results of this evaluation. To ensure the inclusion of high-quality articles, we considered only those with an evaluation score greater than 50.

Data Abstraction

Data abstraction was carried out by four reviewers for each of the included studies, with all results meticulously recorded and managed in a centralized spreadsheet specifically designed for this project. Most of the reviews provided titles for their findings, which were extracted verbatim when available. A summary of the characteristics of each review is presented in Table 2.

Synthesis

For data analysis, the qualitative content analysis method was employed following the three-step approach of Elo and Kyngäs ( 95 ). Given the word limit, it was not possible to reference every article associated with each key concept. Consequently, the tables summarizing the research findings became quite lengthy. The analysis process consisted of the following steps:

• 𝅇 Data Preparation: Key sections of the selected articles were meticulously reread multiple times. With the "paragraph" being considered as the unit of analysis, key concepts related to the research objective were extracted from the text.
• 𝅇 Data Organization: The extracted key concepts were categorized based on their similarities and differences, and subsequently organized into broader, more abstract categories.
• 𝅇 Reporting the Results: The final findings from the data analysis were comprehensively presented in detail in the Results section.

Data Validation

To ensure the accuracy and validity of the qualitative data, we employed the following methods: (a) Presentation of Results to Experts: The analyses and preliminary findings were presented to experts, and their feedback was incorporated. (b) Researcher Consensus: The results were discussed among the four researchers to reach a consensus on the findings.

Results

First, a statistical description of the results will be provided. Subsequently, the findings from the qualitative content analysis will be presented. Figure 3 illustrates the number of published articles categorized by countries.

Figure 3. Distribution of articles by country

The investigation of the geographic distribution of the included studies demonstrated that China had the highest number of articles published in this field, followed by the United States, with Malaysia, Saudi Arabia, and Turkey jointly sharing the third position. Figure 4 illustrates the distribution of the selected articles across various journals.

Figure 4. Distribution of articles by journal

As displayed in Figure 4, the following three journals had the highest number of articles about AI in medical education: International Journal of Educational Technology in Higher Education, Computers and Education: Artificial Intelligence, and Education and Information Technologies, each featuring five articles. Table 3 provides a summary of the status of these journals.

The results of the qualitative content analysis are summarized in Table 4. As outlined earlier, the analysis was conducted in three stages. Initially, the most significant key concepts were identified. Next, these concepts were grouped into themes based on their similarities and differences. Finally, examples of applications were provided for each theme.

As already detailed in Table 4, the review of the included studies led to the identification of the following six main themes: faculty members, students, the teaching and learning process, assessment, curriculum, and management and implementation. Figure 5 depicts the frequency of these themes across the reviewed studies.

Figure 5. Frequency of the identified themes across the reviewed studies

As shown in Figure 5, the highest percentage corresponds to the teaching and learning process, accounting for 25.9%. This is closely followed by management and implementation at 25.6%. Assessment comes next at 14.8%, followed by faculty members and students at 14.2% and 12.6%, respectively. The curriculum holds the lowest percentage, ranking last at 6%.

Discussion

This study aimed to explore AI applications in medical education. To this end, relevant review studies were identified and analyzed. The results of the analysis led to the identification of six main themes, as discussed in detail in the following sections:

§ Faculty Members

In today's global environments, faculty members play a crucial role in contributing to the achievement of higher education objectives ( 96 ). They serve as the vanguard of education, research, and the provision of advisory and entrepreneurial services. By nurturing skilled graduates, faculty members hold a central role in the overall functioning of universities ( 97 ). The integration of AI into education offers significant benefits for faculty members by facilitating improved time management and reduced workload. This, in turn, contributes to increased effectiveness and productivity in fulfilling their responsibilities ( 98 ). Machine learning algorithms assist faculty members in systematically monitoring student performance in class ( 99 ). The faculty can also leverage deep learning to monitor learner attendance through precise facial recognition ( 100 ). Moreover, robots, acting as teaching assistants, provide the faculty with advice and support, thereby enhancing their work quality ( 101 ). The AI’s capability to analyze vast datasets generated from student interactions with educational technologies can provide the faculty members with deep insights into students’ learning behaviors, performance trends, and cognitive tendencies ( 102 ). Additionally, AI can offer guidance, strategies, and recommendations tailored to the educational needs of students, thereby equipping the faculty members with insights into facilitating personalized learning ( 103 ). This support empowers faculty members to identify and address weaknesses in their teaching methods and improve the efficiency of their knowledge transfer promptly, fostering opportunities for self-development ( 104 ). As the reviewed studies indicated, AI presents a broad spectrum of applications for faculty members in the field of medical education with the potential to reduce their workload while simultaneously enhancing the quality of their work and performance ( 18 , 21 , 31 , 40 , 52 ).

§ Students

Medical students and the implementation of programs which aim at their development play a critical role in enhancing their performance and increasing their efficiency as the human capital of the university ( 105 ). One of the key applications of AI for students is supporting personalized learning, where students learn and progress at their own pace and tailor their learning methods with AI assistance. This approach enables students to select and explore topics they are genuinely interested in ( 106 ). Another significant application of AI is its ability to allow for the simulation of educational and practical environments, providing students with an opportunity to practice essential skills. This is particularly valuable in medical education, where AI-powered virtual reality is used for surgical training. AI can also help students identify and acquire the essential skills relevant to the modern workforce, such as programming and data analysis ( 107 ). Moreover, it enhances the support students receive from their instructors, alleviating the anxiety often associated with trial-and-error learning. This nurturing environment fosters lifelong learning while decreasing the stress associated with education. AI systems can also introduce diversity into the educational process, helping students maintain better focus during learning ( 108 ). Furthermore, this technology facilitates opportunities for students to enhance their problem-solving skills and overall learning capacity ( 109 ).

In summary, AI offers a myriad of applications for medical students, significantly supporting their development as the key assets of medical universities by helping them to improve their performance and enhance the quality of their learning experience ( 16 , 19 , 24 , 33 , 50 ).

§ Teaching and Learning Process

The use of AI in higher education leads to the improvement of teaching and learning outcomes. The AI technology enhances student learning by enabling them to confidently use experimental techniques to discover new concepts ( 110 ). Another application of AI in the teaching and learning process is the provision of targeted recommendations to students. The AI facilitates a prompt identification of students who are struggling and the provision of timely interventions ( 111 ). In addition, the development of adaptive learning approaches through AI significantly enhances student engagement in the learning process and improves their academic progress ( 112 , 113 ). AI has also enhanced the effectiveness and efficiency of administrative tasks within the educational process. Overall, it plays an important role in improving teaching and learning processes, contributing to the enhancement of educational quality and learning outcomes in the context of medical education ( 16 , 20 , 21 , 31 , 53 , 113 ).

§ Assessment

Advancements in AI have led to significant transformations in student evaluation systems. Online assessment systems have undergone substantial advancements, particularly following the onset of COVID-19 ( 114 ). AI can serve various monitoring functions in online examinations, such as tracking the frequency of student pauses during a lesson, the time required to answer a question, and the frequency of trial-and-error attempts for a response ( 115 ). AI can also be used for scoring students' written responses or analyzing large and complex datasets ( 116 ). Additionally, neural networks can be utilized to design algorithms that help determine whether a student should practice more similar questions or move to higher or lower difficulty levels ( 111 ). Machine learning algorithms can also be utilized for various assessment-related tasks, such as monitoring student activities, creating models that accurately predict student outcomes, scoring written responses, and analyzing large and complex datasets ( 117 ). As the literature indicates, in the context of medical education, AI offers diverse applications with regard to evaluation systems, from designing personalized questions to providing mechanisms for customizing the evaluation process for each student. This highlights the profound impact of AI on assessment systems and its transformative potential for the future ( 21 , 22 , 36 , 39 , 44 ).

§ Curriculum

AI inspires innovative ideas in course design and instructional design ( 118 ). This technology has diverse applications in medical curricula, such as formulating detailed course objectives, creating content in various formats, generating tailored educational materials for learners with special needs, visualizing course content, producing electronic or digital content, and facilitating the continuous updating of the curriculum content. These significant AI applications substantially enhance the effectiveness and practicality of the curriculum, while facilitating the development of competencies required for the future job market among medical students ( 18 , 32 , 50 , 54 , 71 ).

§ Management and Implementation

AI technology has a wide range of applications in the managerial and executive aspects of medical education and healthcare. For instance, advanced algorithms can be utilized to predict enrollment rates, dropout rates, and student retention, providing valuable insights for university administrators and policymakers. Additionally, AI supports evidence-based decision-making and policy formulation in the healthcare sector by providing diverse data. A significant advantage of AI in management is its potential for substantial cost reductions across various sectors, as highlighted in several studies ( 107 , 119 ). In the field of healthcare, AI offers extraordinary applications in several areas, such as simulation, responsiveness, diagnosis, service quality, and treatment recommendation ( 23 , 29 , 48 , 57 , 72 ).

Limitations

This study excluded grey literature, such as unpublished reports, theses, and books, which may have limited the scope of findings. The inclusion of these resources in future research could provide a more comprehensive overview of AI applications in medical education.

Conclusion

AI has achieved significant milestones in the field of medical education and is poised to play a pivotal role in its future. However, despite its vast potential, AI presents notable challenges and limitations that must be addressed to ensure its effective integration. This study specifically focused on identifying and categorizing the applications of AI in medical education. Beyond these applications, policymakers and planners must prioritize processes that facilitate the seamless integration of AI technologies into educational frameworks. Adapting to the rapid evolution of AI requires flexible strategies and dynamic forward-looking plans. Developing digital competencies and AI literacy among stakeholders is critical for fostering organizational agility, reducing resistance to technological change, and accelerating adoption. A crucial prerequisite for the successful integration of AI into education is cultural readiness. Policymakers and managers should focus on cultivating collective trust and awareness within institutions to create an environment conducive to AI acceptance. Addressing these factors holistically will enable medical education systems to harness the transformative potential of AI while mitigating its associated challenges.

Acknowledgment

Our research team extends heartfelt gratitude to Ms. Nawreen Hena, a graduate of the Master of Higher Education at the University of Oxford, for her invaluable assistance in collecting resources for this study.

Authors’ Contribution

Each author actively contributed to the discussions, reviewed the content, and approved the final manuscript. They collectively take full responsibility for all aspects of the research, ensuring that any questions or concerns related to the accuracy or integrity of any part of the work are addressed comprehensively and resolved effectively.

Conflict of Interest

The authors declare no conflicts of interest

Declaration on the use of AI

Artificial intelligence–based language tools were utilized to assist with translation, improve textual structure, and enhance overall clarity during the preparation and editing of this manuscript. All outputs produced by these tools were carefully evaluated, critically revised, and fully approved by the authors.

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