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 https://doi.org/10.47488/dhrp.v3iS1.94 

 

 
 

 
DHR Proceedings ǀ http://dhrproceedings.org 1 2023, Vol. 3 No. S1 1--5 

 

COMMENTARY 

Bridging the Gap: Integrating Artificial 

Intelligence into Medical Education 
 

Adhira Tippur1 

1Mathematics and Science Academy at the University of Texas Rio Grande Valley, Edinburg, TX 

 

Received: August 15, 2023 

Accepted for publication: September 26, 2023 

Published: October 10, 2023

Introduction 

Artificial intelligence (AI) is rapidly 

becoming an integral part of our lives (1), seamlessly 

integrated into numerous technologies in use today 

(2). Its presence spans from the smartphones we carry 

to the algorithms driving our social media streams. 

Take, for instance, Google Maps, a remarkable AI-

driven tool adeptly guiding us to different destinations, 

showcasing its exceptional navigational prowess in 

our daily experiences. These AI capabilities, when 

integrated with our technologies, make our lives more 

efficient and seamless. This can also be seen in 

medical settings, where AI-enhanced systems 

augment the capabilities of medical personnel in 

diagnosing and treating an extensive array of medical 

conditions. Moreover, AI solutions play a significant 

role in optimizing operational efficiency with 

healthcare institutions, encompassing both medicinal 

functions and administrative responsibilities within 

hospitals (3).  

With AI growing rapidly, it is increasingly 

finding its place in medical educational systems. 

Aspiring medical professionals engage in activities 

such as patient interaction and research, where AI 

subtly and visibly enriches the educational journey (4). 

However, this profound integration prompts 

multifaceted discussions within the medical education 

community (5). While advocates commend AI for its 

potential to enhance learning outcomes by offering 

adaptive and tailored educational pathways (6), 

concerns also arise about striking the right balance 

between AI-assisted learning and the acquisition of 

hands-on clinical skills (7). This paper delves deeper  

 

into the role AI plays in the current medical education 

system.  

Current Applications of AI in Medical 

Education 

Given the expanding scope of medical 

knowledge, integrating technologies like AI into 

medical education is imperative, especially 

considering its transformative potential in medical 

imaging, diagnosis, and treatment (Figure 1). This 

integration offers a pivotal avenue for healthcare 

professionals to apply evolving medical insights in 

their practices. Consequently, cultivating a profound 

understanding of this emerging technology becomes 

indispensable for medical experts in the present 

context. Specifically, in the field of medical education, 

this comprehensive training encompasses not only 

mastering the technology itself but also delving into its 

current merits, which include benefits related to cost-

efficiency, enhanced healthcare quality, and improved 

accessibility (8).  

In the current environment, AI's role in 

medical education predominantly revolves around its 

capacity to deliver personalized feedback, thereby 

providing tailored learning support (9). However, 

there is limited emphasis on refining curricula and 

effectively assessing student progress in academic 

settings. This limitation is intricately linked to ongoing 

challenges stemming from incomplete digitalization 

within education and the intricate nature of 

assessments, both of which demand astute 

management. A study by Lee et al. introduces a 



Special Edition: Junior Commentary 
 https://doi.org/10.47488/dhrp.v3iS1.94 

 

 
 

 
DHR Proceedings ǀ http://dhrproceedings.org 2 2023, Vol. 3 No. S1 1--5 

holistic curriculum and professional development 

framework meticulously tailored for educators (10).  

This curriculum focuses on specific 

categorizations under AI methods in Data Science. 

Specifically, it aims to expose learners to numerous AI 

methods through a structured curriculum divided into 

five distinct units: Data Analytics, Logic Systems 

(using human-readable rules), Machine Learning 

(experienced-based model building), Supervised 

Learning (utilizing neural networks), and Transfer 

Learning (using K Nearest Neighbor algorithm). Each 

unit consists of five lessons carefully designed to 

guide learners through various stages of 

understanding. Starting with hands-on experiential 

learning, these lessons gradually lead to the 

clarification of key concepts including statistical 

analysis, creating AI models, and visualizing data. 

They also include practical exercises conducted in 

Google Colab. This proactive approach, precisely 

attuned to its respective context, aims to empower 

instructors with a nuanced grasp of AI intricacies, 

heightening their sensitivity to the multifaceted ethical 

dimensions inherent in AI, ranging from bias to 

accountability. Impressively, this study underscores 

the successful infusion of AI into STEM classrooms, 

as validated by the enthusiastic endorsement of 

educators who actively engaged with modular 

curriculum units.  

In another study, it was observed that four 

distinct AI techniques (including Machine Learning, 

Deep Learning, Robotic Skills Training, and Virtual 

Reality) are currently employed across various 

medical education domains, with a notable focus 

within training laboratory environments (11). 

Specifically, these techniques were utilized in 

behavioral health, ophthalmology, orthopedics, 

surgery, surgery/medicine, and training labs. This 

shows the diverse areas of implementation in current 

medical education—demonstrating AI’s potential to 

further develop and refine certain medical practices 

(including surgical skills). Letting students practice 

their abilities in simulated situations provides them 

with the perfect avenue to better understand what 

patients might be feeling emotionally and mentally, 

while also fostering effective communication and 

listening skills. For instance, in ophthalmology, the 

integration of Machine Learning and Deep Learning 

proved to be instrumental in aiding students with the 

recognition of numerous eye-associated diseases, 

primarily through medical imaging analysis. This 

helps students identify patterns and improve their 

decision-making abilities, ultimately improving 

medicinal diagnostic precision. Therefore, amid 

ongoing technological evolution, integrating AI into 

medical education offers transformative potential, 

shaping the learning journey for future healthcare 

practitioners. 

 
Figure 1: Various applications of Artificial 

Intelligence in medicine. Created with 

BioRender.com 

Benefits and Challenges of AI in Medical 

Education 

Benefits 

Positioning the upcoming generation of 

medical practitioners to participate in the evolving 

data science revolution by acquiring pertinent 

Machine Learning techniques stands as one of the 

notable benefits of AI in medical education (12). Some 

of these techniques include essential tools such as 

predictive analytics and image detection. Specifically, 

students may initially encounter Machine Learning 

through specialized courses in population health and 

evidence-based medicine. In such contexts, Machine 

Learning serves as a supplementary tool for clinicians, 

enhancing their healthcare delivery. Furthermore, 

technology's adoption opens new opportunities for 

specific student groups, expanding accessibility and 

engagement for part-time students and offering 

tailored programs for gifted or talented students (13). 

Highlighting AI's transformative role in education, 

another study emphasizes its potential to provide 

personalized learning assistance, aligning with 

individual preferences and progress (14). Moreover, 

this examination supports the notion that enhancing 

healthcare quality is achievable by empowering 

medical professionals to reach their full potential (7).  

By incorporating AI-based applications into medical 

education and training, this empowerment journey for 

numerous medical workers can be further facilitated. 

In fact, after implementing AI-based training, 

noticeable enhancements in practical skills were 

observed among medical students (11). 



Special Edition: Junior Commentary 
 https://doi.org/10.47488/dhrp.v3iS1.94 

 

 
 

 
DHR Proceedings ǀ http://dhrproceedings.org 3 2023, Vol. 3 No. S1 1--5 

Challenges 

Integrating AI into medical education 

presents several challenges. Many instructors may 

require additional training to adeptly employ 

technology and enrich the educational experience (15). 

The development of nascent technology, particularly 

when intended for educational purposes, often entails 

substantial expenses and demands a diverse range of 

expertise (13). This expertise includes AI algorithm 

development (designing algorithms tailored to medical 

content), data analytics (collecting, processing, and 

interpreting AI-generated medical education data), 

educational technology integration (seamlessly 

integrating AI into existing curricula), and 

instructional design (creating engaging learning 

experiences). Additionally, embedding AI into 

education can pose difficulties given its 

interdisciplinary nature and reliance on technology 

(14). Among these challenges, as evidenced by 17 

studies, six indicated the presence of early-stage 

prototypes with technical constraints requiring 

refinement to enhance the user experience (16). These 

constraints include aspects such as performance 

enhancement, efficacy validation, and AI 

computational processes. Regarding the efficacy of AI 

applications in medical education, a study emphasized 

the importance of diverse evaluation methods and 

ample sample sizes to validate replicability. This is 

crucial due to the challenges surrounding effective AI 

implementational methods, constraints related to 

available curriculum hours, and limited faculty 

expertise (17). Furthermore, complications arise in 

training AI algorithms due to constraints such as 

limited sample sizes, data integrity, and privacy 

considerations (18). Moreover, concerns have been 

brought up regarding the interpretability, 

generalizability, and potential overfitting of AI 

algorithms (18). Navigating these challenges 

underscores the significance of a comprehensive 

understanding of AI's potential and limitations is vital 

for effective integration in medical education. 

Future Prospects and Considerations 

Significant changes are reshaping medical 

practice due to the progress of cutting-edge 

technologies and Artificial Intelligence (AI). This 

swift and ground-breaking evolution has underscored 

the growing necessity for a comprehensive 

educational curriculum that effectively imparts 

insights to medical students about the possibilities and 

capabilities of AI in the healthcare domain (19). In 

medical education, it's crucial to prioritize essential 

skills such as statistical competence and empathy. 

Aspiring medical students must prepare themselves 

for an innovative educational paradigm driven by AI 

and Machine Learning (20). Leveraging advanced 

technology for student-directed learning involves 

active participation through customization, social 

engagement, and easy access to resources (5). While 

technology holds the potential to transform medical 

education, it's vital to acknowledge that AI cannot 

replace human proficiency and discernment (21). The 

guidance of experienced practitioners remains 

indispensable for medical students to develop clinical 

skills and gain profound subject comprehension (22). 

Recommendations for Effective AI Integration 

Even though AI has rapidly progressed, there 

is a scarcity of readily available literature to help 

medical students grasp its concepts. Existing AI 

resources often assume technical expertise, leaving 

medical students without essential fundamental 

knowledge (23). Therefore, to facilitate the seamless 

integration of AI in the medical field, it is imperative 

to implement strategies that incorporate AI into the 

medical school syllabus (9). This proactive step will 

equip medical professionals with a foundational 

understanding of AI algorithms, enabling them to 

optimize the use of this transformative technology. In 

the context of enhancing patient care through adept 

information technology use, integrating Machine 

Learning-related material can be seamlessly woven 

into a comprehensive curriculum (12). This strategic 

approach ensures a holistic framework that 

emphasizes competence and skill development. 

Additionally, it's crucial to highlight that while AI 

offers diverse advantages, its integration into 

healthcare raises ethical considerations concerning 

data privacy, automation, and telehealth (24). The lack 

of well-defined guidelines can result in challenges 

during implementation, emphasizing the importance 

of establishing a set of rules to be followed. 

Conclusion and Discussion 

Artificial Intelligence (AI) has profoundly 

permeated various aspects of our lives, revolutionizing 

technologies and offering unparalleled advantages. 

This transformative impact extends to medical 

education, where AI-enhanced systems are reshaping 

how medical professionals diagnose, treat, and 

optimize healthcare operations. However, as AI's role 

expands in medical education, discussions arise about 

balancing its benefits with cultivating hands-on 

clinical skills. This calls for an integrated approach 

that harmonizes AI-driven advancements with 

traditional curricula, ensuring that students gain both 



Special Edition: Junior Commentary 
 https://doi.org/10.47488/dhrp.v3iS1.94 

 

 
 

 
DHR Proceedings ǀ http://dhrproceedings.org 4 2023, Vol. 3 No. S1 1--5 

technical proficiency and practical expertise. 

Challenges, including the presence of immature 

prototypes, varying evaluation methods, and ethical 

concerns, underscore the complexity of AI integration. 

To harness AI's full potential, effective integration 

strategies are crucial, equipping future medical 

practitioners with the proficiency needed to navigate 

the evolving healthcare landscape. Within these 

challenges lies a transformative opportunity to 

redefine medical education, enhancing healthcare 

professionals’ capabilities and elevating patient care to 

new heights. As AI continues to evolve, the journey 

towards comprehensive AI integration in medical 

education is set to shape the future of healthcare. 

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