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Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review

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Fatima Nagi
Fatima Nagi
Fatima Nagi
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Rawan Salih
Rawan Salih
Rawan Salih
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Mahmood Alzubaidi at Hamad bin Khalifa University
Mahmood Alzubaidi
Hurmat Shah
Hurmat Shah
Hurmat Shah
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Abstract

Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.
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Applications of Artificial Intelligence (AI)
in Medical Education: A Scoping Review
Fatima NAGIa, Rawan SALIH a , Mahmood AlZUBAIDIa, Hurmat SHAHa,
Tanvir ALAMa, Zubair SHAHa and Mowafa HOUSEHa,1
a College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Abstract. Artificial Intelligence (AI) is increasingly used to support medical
students' learning journeys, providing personalized experiences and improved
outcomes. We conducted a scoping review to explore the current application and
classifications of AI in medical education. Following the PRISMA-P guidelines, we
searched four databases, ultimately including 22 studies. Our analysis identified four
AI methods used in various medical education domains, with the majority of
applications found in training labs. The use of AI in medical education has the
potential to improve patient outcomes by equipping healthcare professionals with
better skills and knowledge. Post-implementation refers to the outcomes of AI-based
training, which showed improved practical skills among medical students. This
scoping review highlights the need for further research to explore the effectiveness
of AI applications in different aspects of medical education.
Keywords. Artificial Intelligence, Machine Learning, Medical Education.
1. Introduction
The healthcare industry, in particular medical education, is being transformed by
artificial intelligence (AI). AI has the potential to transform medical education by
delivering personalized and adaptive learning experiences, increasing diagnosis accuracy,
and facilitating data-driven decision-making [1]. Medical education has typically been
one-size-fits-all, with students required to memorize large volumes of knowledge.
However, AI can assist in tailoring the learning experience to the needs of the particular
student, allowing them to focus on areas where they require more practice [2]. AI can
also assist instructors in creating individualized learning programs, tracking learner
progress, and providing real-time feedback [3]. While many published reviews report the
specific type of AI and its effectiveness in the medical education process, however; this
review aims to provide an overview of AI in the medical education domain, focusing on
the type of Al methods used, classification, and area of implementation.
2. Methodology
This scoping review was conducted following the guidelines from the Preferred
Reporting Items for Systematic Review and Meta-Analysis (PRISMA-ScR) [4]. We
1 Corresponding Author: Dr. Mowafa Househ, College of Science and Engineering, Hamad Bin Khalifa
University, Doha, Qatar, E-mail: mhouseh@hbku.edu.qa.
Healthcare Transformation with Informatics and Artificial Intelligence
J. Mantas et al. (Eds.)
© 2023 The authors and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
doi:10.3233/SHTI230581
648
searched four databases: (PubMed, Google Scholar, Scopus, and ScienceDirect). The
search strategy, which included population (i.e., medical students) and target intervention
(i.e., artificial intelligence), was applied to the most used database listed earlier
(Appendix A2). To minimize bias errors, the study selection and data extraction
processes were carried out independently by the reviewers, thus increasing the validity
and reliability of the work. The study selection process consisted of three steps: removing
the duplicates in the 1485 retrieved studies, screening titles and abstracts, and reading
the full text of the remaining studies. We then extracted data into an Excel sheet, which
was synthesized using a narrative approach. Then, all included papers were summarized
into simplified text and tables (Appendix B2).
3. Results
A total of 22 studies were included out of 1485 retrieved studies. Amongst these, 1220
duplicate studies were excluded. Through title and abstract reading, 47 studies were
excluded, and 25 studies were excluded following the exclusion and inclusion criteria of
being non-English studies, non-peer-reviewed articles, or scoping review text. Through
full-text screening of the remaining studies, we end up with 22 studies that meet the
scoping illegibility criteria. We also summarize the key characteristics of the selected
studies (Details in Appendix C2). The demographics and other specific details can be
found in (Details in Appendix D2). The results reveal that three AI branches were
identified across different domains in the medical education field. Machine learning
algorithms and virtual reality were the most commonly used ML and Dl methods (n=11),
followed by Virtual Reality (n=9) and robotic skills training (n=2). These AI methods
were applied across various medical education domains, with the majority found in
training labs (n=10), followed by the surgery domain (n=7), orthopedics (n=2),
ophthalmology (n=1), surgery/medicine (n=1), and behavioral health (n=1). The selected
studies discussed the different AI technologies, evaluation metrics (Details in Appendix
E2), and areas of implementation, as summarized in Table 1.
Table 1. Type of AI technologies used in the medical education domain.
Area of
Implementation
Machine
Learning/
Deep
Learning
Robotic s
kills
Training
Virtual
Reality
Grand
Total
Behavioural Health
1
1
Ophthalmology
1
1
Orthopaedics
1
1
2
Surgery
3
2
2
7
Surgery/Medicine
1
1
Training Lab
6
4
10
Grand Total
11
2
9
22
2 https://doi.org/10.5281/zenodo.7866503
F. Nagi et al. / Applications of Artificial Intelligence (AI) in Medical Education 649
The results demonstrate the application of AI techniques in various domains of medical
education. In each domain, AI methods such as Machine Learning/Deep Learning,
Robotics Training, and Virtual Reality have been implemented to enhance the learning
experience and improve outcomes. In the domain of Behavioral Health, Virtual Reality
has been used as a tool to create immersive and interactive environments, allowing
students to practice their skills in simulated scenarios that mimic real-world situations.
This approach helps learners better understand patients' psychological and emotional
needs and fosters empathy and effective communication skills. In Ophthalmology,
Machine Learning/Deep Learning has been applied to assist students in recognizing and
diagnosing various eye diseases and conditions using medical images. The technology
enables students to identify patterns and learn from large datasets, thereby improving
their diagnostic accuracy and decision-making abilities. In Orthopedics, both Machine
Learning/Deep Learning and Robotics Training have been employed. Machine
Learning/Deep Learning aids in predicting patient outcomes, optimizing treatment plans,
and identifying potential complications.
Robotics Training, on the other hand, provides hands-on experience for students to
develop their surgical skills and precision, resulting in better patient outcomes. Surgery
is a domain where all three AI techniques have been utilized. Machine Learning/Deep
Learning assists in preoperative planning, diagnosis, and predicting patient outcomes.
Robotics Training is used for developing surgical skills, including robotic-assisted
surgeries, improving precision and minimizing surgical errors. Virtual Reality creates
immersive surgical simulations that allow students to practice and refine their techniques
in a safe environment. In the Surgery/Medicine domain, Virtual Reality has been
employed to create realistic simulations of various surgical and medical procedures,
enabling students to gain experience and improve their clinical decision-making skills
without the risks associated with real-life procedures. In Training Labs, Machine
Learning/Deep Learning and Virtual Reality have been widely used. Machine
Learning/Deep Learning allows for personalized learning experiences by analyzing
student performance data and identifying areas for improvement. Virtual Reality offers
immersive training simulations, enabling students to practice their skills in a controlled
and risk-free environment.
4. Discussion
The scoping review aims to emphasize the current applications and branches of AI in the
medical education process. Among the 22 included studies, approximately 10 studies
implemented various AI methods within training labs for medical students. The
evaluation metrics were promising, as evident by the positive feedback from trainees.
The post-implementation results demonstrated that AI-based training supported the
enhancement of practical skills for medical students, employing a modern approach
using artificial intelligence-based assistance. Additionally, the review highlights the
diverse range of medical domains in which AI is being applied, including training labs,
surgery, orthopedics, ophthalmology, surgery/medicine, and behavioral health. This
indicates the adaptability of AI technologies, which can be tailored to specific fields and
educational requirements.
Incorporating AI in medical education has the potential to revolutionize traditional
teaching methods by offering personalized, adaptive learning experiences. AI-driven
tools can identify students' strengths and weaknesses, enabling the development of
F. Nagi et al. / Applications of Artificial Intelligence (AI) in Medical Education650
customized learning plans that target specific areas for improvement. This targeted
approach can enhance knowledge retention and skill development, resulting in better-
prepared healthcare professionals. Another notable aspect of AI integration in medical
education is its capacity to provide real-time feedback and assessment. This allows
students to track their progress, identify areas of weakness, and receive immediate
guidance for improvement. Instructors can also benefit from AI-generated analytics,
which can help them identify trends and patterns in student performance, adjust teaching
strategies accordingly, and optimize the learning process. Despite the promising results
and potential of AI in medical education, there are still areas that require further
exploration and research. These include investigating the long-term effects of AI-assisted
learning on student performance, evaluating the impact of AI-driven tools on instructor-
student interactions, and exploring the ethical considerations of incorporating AI
technologies in medical education.
5. Conclusion and Future Direction
With the increasing adoption of AI in the healthcare industry, it is crucial to incorporate
this technology into medical education to ensure that healthcare professionals acquire
the necessary skills for providing high-quality care in the future. This review offers
valuable insights into recent AI applications in medical education and demonstrates their
potential to improve learning outcomes, enhance students' confidence, and develop
superior surgical skills. Consequently, we strongly emphasize the need for conducting
further research to explore the effectiveness of AI applications in medical education. This
exploration could potentially transform the entire educational system for medical
students, given that AI is a rapidly growing field with numerous new advancements on
the horizon.
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F. Nagi et al. / Applications of Artificial Intelligence (AI) in Medical Education 651
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Using a videogame to facilitate nursing and medical students first visit to the operating theatre. A randomized controlled trial
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  • May 2017
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