Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Apr 23;38(5):552–567. doi: 10.1002/ca.24272

The Roles of Artificial Intelligence in Teaching Anatomy: A Systematic Review

Tanisha S Joseph 1, Shelleen Gowrie 1, Michael J Montalbano 1, Stephan Bandelow 2, Mark Clunes 2, Aaron S Dumont 3, Joe Iwanaga 3,4,5, R Shane Tubbs 1,3,4, Marios Loukas 1,6,7,8,✉
PMCID: PMC12163106  PMID: 40269576

ABSTRACT

Anatomy education is a cornerstone of medical training and relies on cadaveric dissection and 2D illustrations. Technological advancements and integrated curricula have reduced the focus on detailed anatomy and challenged educators to engage Generation Z learners with interactive, tech‐driven methods. Advanced imaging and artificial intelligence (AI) offer a solution, providing virtual dissection simulations and personalized learning tools that mimic 3D anatomy and adapt to individual student needs. Machine learning, a subset of AI, enhances this process by enabling predictive analytics, adaptive feedback, and tailored learning pathways based on performance data, significantly improving anatomical comprehension. Despite its benefits, AI integration raises concerns about over‐reliance on technology, biases, and diminished human interaction in training. This review examines AI's transformative potential in anatomy education while emphasizing the need for balanced implementation and ethical oversight. A systematic review following PRISMA guidelines was conducted, utilizing PubMed and backward citation searches. The search yielded 56 studies, with 47 additional articles from citations, resulting in 61 included studies. These explored AI applications such as virtual dissection simulations, machine learning algorithms for adaptive feedback, and gamified learning experiences, which were shown to enhance engagement, personalize learning, and improve anatomical understanding. Concerns about over‐reliance on AI and the loss of human interaction were also raised. AI has the potential to enhance anatomy education, but careful consideration of ethical and practical implications is essential. A balanced approach combining traditional methods with AI and robust oversight is crucial for effective integration.

Keywords: anatomical education, artificial intelligence, humans, machine learning, simulation

1. Introduction

Anatomy education is a cornerstone of medical training, and provides the foundational knowledge essential for accurate diagnoses, safe surgical interventions, and effective interpretation of medical imaging. Traditionally, anatomy education primarily relied on two‐dimensional (2D) illustrations from textbooks and lectures, with hands‐on exposure to three‐dimensional (3D) structures being confined to cadaveric dissection in wet labs (Patra et al. 2023; Iwanaga et al. 2021). Incorporating advanced technology and integrated systems curricula in medical schools has de‐emphasized dedicated curricular time for detailed anatomical education (Bankar et al. 2024; Taranikanti and Davidson 2023).

Current students have extensive digital technology exposure during all phases of pre‐tertiary education and are habituated to succinct video‐based information browsing, posing a challenge to tertiary‐level educators in meeting the attention and engagement requirements of Generation Z learners (Taranikanti and Davidson 2023). This evolving landscape in the education sector in general and specifically in anatomical medical education may pose a risk if diminished focus on anatomy could compromise patient care and safety.

Integrating artificial intelligence (AI) generated resources into anatomy education has emerged as a promising solution to address these challenges and align with modern educational demands. AI refers to the use of computer algorithms and machine learning techniques to simulate human cognitive functions (Chen and Decary 2020) which can then be used in the generation of text, images, language, and simulated interactions, pattern recognition, etc. Recently, this mimicry has transitioned from general problem‐solving to being more applicable in specific fields, including healthcare and education, as evidenced by its use in ultrasound‐guided anatomy training (Jacobs et al. 2023), simulation‐based learning for clinical education (Mazhar et al. 2024), and gamified platforms that enhance anatomy engagement (Castellano et al. 2024).

Within anatomy education, AI has the potential to redefine traditional teaching paradigms by offering transformative tools that foster deeper understanding, engagement, and proficiency among students. For example, AI enables adaptive assessment, where algorithms analyze individual student performance and knowledge gaps to deliver customized learning pathways and targeted feedback (Chan and Zary 2019; Leng 2024; Tolsgaard et al. 2023). This precision education ensures that learners receive content tailored to their unique needs, facilitating mastery of complex anatomical concepts at their own pace. Additionally, AI‐powered platforms, such as virtual dissection tools, simulate realistic 3D anatomical structures, allowing students to practice and visualize spatial relationships in ways that traditional 2D methods or cadaveric dissections alone cannot achieve (Mazhar et al. 2024; Lazarus et al. 2019; Bankar et al. 2024).

Machine learning, a subset of AI, further enhances this process by identifying patterns in student performance, predicting areas of difficulty, and dynamically adjusting instructional strategies to optimize learning outcomes (Li et al. 2021; Lee 2024; Mavrych et al. 2024). Beyond assessments, AI can automate the creation of highly interactive and gamified learning environments, improving student engagement and motivation, particularly among Generation Z learners who thrive on technology‐driven education (Castellano et al. 2024; Rodrigues et al. 2022; Totlis et al. 2023). By integrating such capabilities, AI addresses the challenges posed by reduced curricular time for anatomy and limited access to traditional resources, ensuring that students receive a robust and personalized educational experience.

Furthermore, the COVID‐19 pandemic accelerated the adoption of new technologies, as many medical schools had to limit access to campuses due to social distancing and workplace closures, and de facto cadaveric dissection was restricted (Bankar et al. 2024). Even before the recent pandemic, anatomy education faced challenges with limited cadaver availability and rising costs, which led institutions to explore anatomy resource alternatives that included AI, virtual reality (VR), and robotics as essential elements in post‐pandemic anatomy teaching (Bankar et al. 2024; Iwanaga et al. 2023; Miltykh et al. 2023).

Driven by these modern challenges, such as the global shortage of cadavers, AI generated resources/educational experiences have emerged as a transformative alternative. These resources offer solutions such as virtual dissection simulations and personalized learning algorithms. These tools can supplement the traditional cadaver‐based instruction, providing an interactive, 3D approach that mimics anatomical structures (Lazarus et al. 2019; Williams et al. 2025; Bankar et al. 2024).

Moreover, AI's integration into anatomy education extends beyond visualizing anatomical structures in a digital environment. It offers dynamic, personalized learning experiences by analyzing individual student performance to deliver tailored feedback and recommendations, enabling a deeper, more adaptive understanding of the student and their mastery of anatomy. This transformative shift is driven by advancements in machine learning, enhanced graphics processing, and the availability of large, annotated datasets, which allow AI systems to generalize, reason, and learn from experience (Bankar et al. 2024). These developments position AI as a scalable solution to overcome challenges in traditional cadaver‐based learning, while also revolutionizing curriculum design, knowledge acquisition, and the assessment of student performance.

However, concerns exist regarding extending AI systems within medical curricula. While AI has the potential to enhance learning, educators worry that its overuse could diminish the essential human interaction critical to effective teaching and learning or even limit a student's ability to self‐reflect and self‐assess in the absence of AI‐assisted learning. Additionally, inadequate training on AI use could perpetuate biases already present in medical education, ultimately exacerbating discrimination in healthcare practice. Therefore, educators must engage with AI effectively to ensure it enhances rather than detracts from the learning experience (Lazarus et al. 2024).

Through a comprehensive examination of existing literature, this review aims to provide a nuanced understanding of how AI resources are reshaping the landscape of anatomy education and charting a course for medical educators toward a more immersive and effective learning experience.

2. Materials and Methods

When searching through publications, the authors followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. PubMed was searched using 1 query: (“Artificial intelligence” AND “Anatomy education”) up to November 2024. Backward citation searching within retrieved articles was also performed to locate additional articles. Articles were included if they explicitly discussed the application of AI and gross anatomy education. Exclusion criteria pertained to articles that: (1) Discussed histopathology instead of gross anatomy, (2) focused on veterinary rather than human anatomy, and (3) discussed AI or anatomy education, but not in conjunction.

3. Results

The initial search identified 56 studies, supplemented by 47 additional articles through backward citation searches expanding the scope of this review. A comprehensive screening process was performed according to the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines, resulting in 71 studies being selected for full‐text review. Ultimately, 61 studies met the inclusion criteria, including 14 from the initial search and 47 from backward citations. Based on their study design, 43 were original research articles, 2 were guidelines or frameworks, and 2 were editorials.

In this systematic review, the included studies explored diverse applications of AI in anatomy education. These studies were categorized into five thematic areas presented in Table 1 through Table 2, reflecting AI's diverse applications in anatomy education.

TABLE 1.

AI in teaching and learning tools for an anatomy education.

Study Focus Findings Applications
Abdellatif et al. (2022) Teaching and assessing anatomy with AI Discussed AI's role in improving teaching, learning, and assessment AI‐enhanced anatomy education and assessment
Castellano et al. (2024) Gamification and AI in anatomy education Demonstrated improved engagement through gamified AI platforms Gamification in anatomy education using AI
Chan and Pawlina (2019) Debate on AI in anatomy education Discusses balancing AI's benefits with maintaining human‐driven teaching Integrating AI with traditional teaching.
Ilgaz and Çelik (2023) AI platforms for anatomy education Showed ChatGPT and Bard's potential for enhancing anatomy learning AI‐powered anatomy education platforms
Li et al. (2021) AI‐powered chatbots for anatomy education Demonstrates the potential of AI chatbots in assisting anatomy learning AI chatbots for anatomy education
Miltykh et al. (2023) Virtual reality in anatomy education Highlights VR's transformative role during the COVID‐19 pandemic in anatomy education VR as a key technology in anatomy education
Patra et al. (2023) Visualization techniques in anatomy Highlighted advancements in anatomical visualization methods Advanced visualization tools for anatomy learning
Williams et al. (2025) Augmented reality in anatomy education Found augmented reality (AR) to be effective and comparable to traditional methods for learning AR as a complementary tool for anatomy learning.
Yannier et al. (2020) Mixed‐reality AI system in STEM education Showed significant improvements in learning outcomes through real‐time feedback AI‐enhanced STEM and anatomy education
Open in a new tab

Note: This table summarizes studies focusing on the development and implementation of AI‐driven tools for anatomy education, including visualization techniques, simulation‐based learning, and chatbot applications. Key findings highlight AI's role in improving teaching methodologies and facilitating deeper student understanding of anatomical concepts.

TABLE 2.

AI for personalization and student engagement.

Study Focus Findings Applications
Chan and Zary (2019) Challenges of AI in medical education Discussed AI's role in personalizing anatomy education Personalized anatomy learning using AI
Han et al. (2019) Medical education trends with advanced technologies and AI. Discussed how AI tools can prepare future physicians through personalized and adaptive learning Strategic incorporation of AI for student‐centered anatomy education
Iwanaga et al. (2021) Anatomy education during COVID‐19 Reviews anatomy education innovations combining traditional and modern methods post‐pandemic Hybrid teaching approaches in anatomy education
Khalil and Roe (2023) Emotion detection in anatomy education Explored AI's ability to monitor student emotions to improve engagement Emotion‐based engagement in anatomy education
Lazarus et al. (2019) Modernizing anatomy education Highlighted AI's ability to enhance teaching methodologies AI‐enhanced curriculum innovation
Lee (2024) ChatGPT's role in engagement Demonstrated ChatGPT's ability to personalize feedback and enhance student learning. Generative AI for personalized anatomy learning.
Leng (2024) Challenges and integration of AI Explored future opportunities and challenges in personalized anatomy learning Strategic integration of AI in anatomy curricula
Lindqwister et al. (2021) AI‐RADS: AI in resident training Proposed AI‐driven curricula for medical residents in anatomy education AI‐based structured anatomy curricula for medical residents
Liu et al. (2024) Digital pedagogical support systems Demonstrated how AI enhances equity and engagement in education AI‐enhanced tools for personalized learning
Nguyen et al. (2024) Enhancing engagement with generative AI Highlighted AI's transformative potential to boost engagement and learning outcomes Generative AI for personalized and interactive learning
Ottone et al. (2021) Empowering human anatomy education through gamification and AI Explores how gamification and AI can enhance engagement in anatomy learning AI‐based gamification for anatomy education
Shaikh et al. (2022) Virtual classrooms powered by AI Demonstrates AI's potential in creating sustainable digital learning environments AI‐powered virtual classrooms for anatomy education
Taranikanti and Davidson (2023) AI‐driven anatomy chatbots and metacognition Demonstrates the effectiveness of AI chatbots in fostering reflective learning AI chatbots for metacognitive skills in anatomy education
Totlis et al. (2023) ChatGPT and AI's role in anatomy education Highlights the role of generative AI in improving anatomy understanding Generative AI tools for anatomy education
Yang et al. (2021) Human‐centered AI for education Highlights AI's ability to provide personalized and student‐focused support Student‐focused anatomy education using human‐centered AI
Open in a new tab

Note: This table presents studies that explore AI's capabilities in tailoring educational experiences to individual learners and fostering student engagement. Key themes include emotion‐based learning, personalized feedback, and adaptive curriculum designs.

3.1. AI in Teaching and Learning Tools

Multiple studies highlighted AI's ability to enhance teaching tools, including virtual simulations and chatbots, to improve anatomy education (see Figure 1). For example, Abdellatif et al. (2022) discussed the potential of AI for teaching and assessment, while Castellano et al. (2024) demonstrated improved engagement through gamified AI platforms (See Table 1). Miltykh et al. (2023) emphasized VR's role during the COVID‐19 pandemic, showcasing its transformative potential.

FIGURE 1.

FIGURE 1

Open in a new tab

Interconnections between AI applications in anatomy education: This network diagram illustrates how different AI applications interact to enhance anatomy education by creating an integrated and adaptive learning ecosystem. The relationships highlight keyways AI‐driven tools support personalized learning, visualization, assessment, and feedback mechanisms.

3.2. AI for Personalization and Student Engagement

Studies in this category addressed AI's capacity to provide personalized and adaptive learning experiences, tailoring feedback to individual students. Khalil and Roe (2023) explored AI's ability to monitor emotions to enhance engagement, while Lee (2024) demonstrated ChatGPT's role in providing interactive support and real‐time feedback (See Table 3).

TABLE 3.

AI in assessment and feedback.

Study Focus Findings Applications
Al‐Khater (2024) Comparative performance of AI platforms on anatomy‐based assessments Highlighted ChatGPT's 100% accuracy on anatomy questions and Claude's superior radiographic interpretations AI‐driven evaluation tools for anatomy learning and assessment
Ayers et al. (2023) AI chatbots in medical education Compared AI and physician responses, showing AI's efficacy in feedback AI‐enhanced feedback systems in anatomy education
Collins et al. (2024) AI‐based tutoring systems Showed enhanced assessment outcomes with AnatomyGPT AI for formative and summative anatomy evaluations
Kung et al. (2023) AI in USMLE‐style assessments Showed ChatGPT's ability to assist students in medical exam preparation AI‐assisted exam preparation for anatomy learning
Moshirfar et al. (2023) Comparing GPT‐3.5, GPT‐4, and human expertise Demonstrates GPT‐4's superior performance in answering anatomy‐related questions AI‐assisted learning tools for anatomy
Wittich et al. (2013) Measuring professionalism in anatomy education Validated methods to assess critical reflections in anatomy students AI‐driven assessment tools for anatomy
Tolsgaard et al. (2023) AI in medical education research Provided foundational principles for AI‐based assessment strategies Research frameworks for anatomy education
Zawacki‐Richter et al. (2019) AI applications in higher education Systematic review of AI's potential to revolutionize anatomy learning Comprehensive integration of AI tools in anatomy teaching
Open in a new tab

Note: This table outlines studies evaluating AI's role in anatomy assessments, emphasizing the use of AI for formative feedback, exam preparation, and ensuring objective evaluation. Applications include AI‐driven tutoring systems and automated performance analysis tools.

3.3. AI in Assessment and Feedback

Studies have also shown AI's effectiveness in improving assessment methodologies. Al‐Khater (2024) demonstrated ChatGPT's 100% accuracy in knowledge‐based anatomy assessments, and Collins et al. (2024) introduced AnatomyGPT as a tool for enhancing formative and summative evaluations (See Table 4).

TABLE 4.

Ethical and practical implications of AI in anatomy education.

Study Focus Findings Applications
Cornwall et al. (2024) Ethical concerns in AI‐driven anatomy education Raised ethical concerns about replacing cadaver dissection with AI Ethical guidelines for AI in anatomy
Eysenbach (2023) Generative AI in medical education Highlighted the ethical implications of using generative AI tools Balancing AI benefits with ethical risks
Ghai et al. (2020) Explainable active learning (XAL) Demonstrated the importance of transparent AI systems for learning Trust‐building and ethical AI integration
Guimarães et al. (2017) Challenges of integrating AI Explored ethical and logistical barriers to AI integration in anatomy Addressing ethical considerations in AI teaching
Iwanaga et al. (2023) Metaverse in anatomy education Questions the necessity and practicality of Metaverse for anatomy education; discusses survey results Evaluating new technologies like the Metaverse
Lazarus et al. (2024) Promises and risks of AI in clinical anatomy education Explores the benefits and limitations of AI tools in anatomy education Balancing AI's potential and challenges in anatomy education
Masters (2023) Ethical use of AI in medical education Provided guidelines for responsible AI integration in education. Ethical frameworks for AI implementation.
Rizzolo et al. (2010) Curriculum innovation through AI Discusses how AI can modernize anatomy course design and teaching strategies AI‐enhanced curriculum development
Rechowicz and Elzie (2024) Emotional detection through AI in anatomy education Highlights AI's ability to detect and respond to student emotions AI for tracking emotional engagement in anatomy classes
Vázquez et al. (2005) Teaching challenges in human anatomy Explores how AI helps overcome barriers to effective anatomy education AI for improving anatomy teaching strategies
Open in a new tab

Note: This table discusses studies addressing the ethical and logistical challenges of integrating AI into anatomy education. Topics include ethical considerations surrounding cadaver replacement, data biases, and the importance of preserving human interaction in teaching.

3.4. Ethical and Practical Implications of AI in Anatomy Education

This category covered the challenges and ethical considerations of integrating AI in curricula. Cornwall et al. (2024) raised ethical concerns about replacing cadaver dissection with AI simulations. Rechowicz and Elzie (2024) highlighted the use of AI for tracking emotional engagement, and Masters (2023) provided guidelines for ethical AI integration in anatomy curricula (See Table 5).

TABLE 5.

AI in emerging technologies for anatomy education.

Study Focus Findings Applications
Asghar et al. (2022) Humanoid robots in anatomy education Highlighted humanoid robots as innovative teaching aids Robotics integration in anatomy classrooms
Bankar et al. (2024) New technologies in teaching anatomy Reviewed VR, robotics, and AI in post‐pandemic anatomy education Emerging AI tools for anatomy curricula
Fajrianti et al. (2022) Augmented intelligence in anatomy education Demonstrated improved learning through body tracking technology Interactive anatomy learning with augmented AI
Jacobs et al. (2023) AI in ultrasound‐guided anatomy education. Demonstrated AI's role in improving practical skills through ultrasound training tools Integration of AI for practical anatomy training with imaging technologies
Jaffer et al. (2022) Potential of humanoid robots in anatomy education Reviews the use of humanoid robots to improve anatomy education Humanoid robots for interactive anatomy learning
Mavrych et al. (2024) Comparative analysis of AI platforms ChatGPT‐4 outperformed competitors in anatomy question accuracy Comparative evaluation of AI tools for anatomy education
Mazhar et al. (2024) Simulation‐based and AI‐based learning Highlights the benefits of simulation‐based and AI‐driven anatomy education Simulation and AI integration in anatomy education
Noel (2023) AI‐powered text‐to‐image generators Found AI‐generated illustrations effective for learning anatomy Visual aids in anatomy education using AI
Richardson et al. (2021) AI in radiology‐integrated anatomy education Reviews AI's role in radiology and its potential for anatomy education Integrating AI with radiology‐based anatomy education
Rodrigues et al. (2022) AI‐driven gamification in anatomy education Explores how AI can enhance gamified learning experiences Gamified anatomy learning tools with AI
Stanford Medicine  Future role of AI in healthcare and medical education Predicts AI's transformative impact on medical training Strategic integration of AI in anatomy education
Sugand et al. (2010) Modernizing anatomy education Highlights AI's ability to reshape traditional teaching methods Modernizing anatomy teaching with AI tools
Turney (2007) AI in modern medical curricula Explores the digitization of anatomy teaching through AI Digitized anatomy education platforms
Urh et al. (2015) Yang, S. J. H., et al. (n.d.) Reviews AI's contribution to personalized gamification strategies Gamification in anatomy education with AI
Open in a new tab

Note: This table captures studies examining the application of cutting‐edge AI technologies, such as augmented intelligence, humanoid robots, and virtual reality, in anatomy education. Highlights include enhanced practical training and gamified learning experiences facilitated by AI.

3.5. AI in Emerging Technologies for Anatomy Education

The final theme highlighted innovative uses of AI, such as humanoid robots and simulation‐based learning. Asghar et al. (2022) emphasized the use of humanoid robots in anatomy teaching, while Mazhar et al. (2024) and Noel (2023) discussed the role of AI in simulation‐based education and text‐to‐image generators for anatomical illustrations (See Table 5).

These studies collectively emphasize AI's transformative potential to modernize anatomy education through enhanced visualization, personalized learning, gamification, and assessment tools (See Figure 2). However, ethical and practical challenges must be addressed to ensure balanced and effective integration.

FIGURE 2.

FIGURE 2

Open in a new tab

Proportion of AI studies across application categories: This pie chart illustrates the distribution of research studies focusing on different AI applications in anatomy education. Personalization and engagement leads with 15 studies, followed by emerging technologies (14), ethical and practical implications (10), teaching and learning tools (9), and assessment and feedback (8). The data highlights the growing emphasis on AI‐driven personalized learning and technological advancements in medical education.

4. Discussion

Incorporating AI in anatomy education has shown promise but also challenges and limitations. Several studies have been employed to provide a comprehensive understanding of the roles AI can fill in teaching anatomy, highlighting its strengths, opportunities, and ethical concerns. Recent contributions to the literature further expand on these themes.

4.1. Harnessing AI for Enhanced Anatomy Education: Strengths, Opportunities, and Future Directions

4.1.1. Personalized and Adaptive Learning

AI‐driven educational platforms have demonstrated significant potential in tailoring learning experiences to individual students' needs, particularly in the context of complex anatomical concepts. By leveraging advanced algorithms, these systems generate real‐time, personalized feedback based on students' performance, bridging the gap between traditional and digital teaching methods (Bankar et al. 2024). AI‐powered tools like chatbots and intelligent tutoring systems enhance this adaptability by fostering interactive, dialogue‐based learning environments. These tools provide immediate, conversational feedback, promoting deeper engagement and more effective comprehension (Li et al. 2021; Patra et al. 2023). Recent advancements, as discussed by Lee (2024), highlight ChatGPT's ability to provide personalized, real‐time feedback, simplifying complex anatomical concepts based on students' pace and understanding levels. Lee (2024) emphasized that ChatGPT can adapt its explanations to students' knowledge levels, further highlighting its value in promoting personalized learning. ChatGPT's ability to track student progress and provide immediate feedback facilitates a deeper understanding of anatomical concepts, especially for students struggling with complex topics.

Recent studies demonstrate the varied capabilities of AI platforms in providing personalized learning experiences. For example, Mavrych et al. (2024) conducted a detailed comparative analysis of ChatGPT‐4, Copilot, PaLM, and Bard for medical education, finding ChatGPT‐4 to be statistically superior in answering anatomy questions with a 60.5% accuracy rate, significantly outpacing its counterparts. This underscores its potential for accurate, adaptive, and efficient responses to complex anatomy queries. Similarly, Al‐Khater (2024) highlighted ChatGPT's ability to achieve 100% accuracy in anatomy questions consistently, outperforming other platforms like Gemini, which lagged with a 60% accuracy rate. These findings solidify ChatGPT's role as a leading tool for personalized learning in anatomy education.

Building on this, Collins et al. (2024) introduced AnatomyGPT, a customized AI tutor that demonstrated improved performance on standardized anatomy questions compared to general‐purpose AI like ChatGPT. This tool provides accurate answers and cites reputable sources, addressing concerns over content reliability, offering a robust model for intelligent tutoring systems, and enhancing trust among students and educators. These findings suggest that integrating customized AI models into anatomy curricula could redefine how anatomy is taught and learned. Moreover, Arun et al. (2024) compared ChatGPT with Anatbuddy, a specialized anatomy chatbot. Their study found that Anatbuddy outperformed ChatGPT in user satisfaction and accuracy, showcasing the benefits of domain‐specific AI customizations in fostering better student engagement and comprehension. This underscores the potential for tailored AI applications to address specific educational needs while increasing engagement.

Humanoid robots equipped with AI have also been proposed as innovative teaching aids that simulate patient interactions, perform repetitive tasks, and provide up‐to‐date anatomical information (Asghar et al. 2022). Additionally, AI can detect and respond to gaps in students' knowledge, allowing virtual facilitators to cater to specific learning needs through customized feedback and one‐on‐one instruction (Patra et al. 2023). The adaptability of these systems helps bridge the gap between traditional teaching and the needs of the digital generation.

Moreover, AI tools like ChatGPT are not just limited to answering questions but also offer comprehensive learning aids. AI has been instrumental in tracking student progress and adjusting instructional strategies to fit individual learning levels. By simplifying and explaining complex concepts based on the learner's pace, these tools allow for a personalized educational experience (Leng 2024). AI's ability to generate test questions tailored to students' learning outcomes further reinforces knowledge and helps them identify areas requiring further focus (Leng 2024). For instance, Mavrych et al. (2024) noted ChatGPT‐4's capability to generate detailed clinical scenarios and related MCQs tailored to student needs, reflecting a personalized approach that bridges theoretical knowledge and clinical application. This adaptability extends beyond mere knowledge retention to include emotional and behavioral responses, as AI is increasingly used to analyze students' emotions during gross anatomy education. By monitoring facial expressions, voice tones, and textual data, AI can help educators identify when students need additional support (Khalil and Roe 2023), enhancing the learning experience. These findings highlight the increasing relevance of AI platforms in anatomy education, particularly for medical students requiring tailored learning paths.

4.1.2. Engagement Through Gamification and Interactive Learning

Evidence has shown that combining AI and gamification can effectively increase engagement with anatomical content by making learning more interactive and enjoyable. Ottone et al. (2021) described how gamification elements such as points, leaderboards, and challenges can be easily integrated into AI‐driven educational platforms. These elements create a competitive and rewarding environment that may activate both intrinsic and extrinsic motivation, encouraging students to participate, to excel, and to compete with self or others in retaining anatomical knowledge, ultimately preparing them for clinical practice.

Interactive learning and student engagement can enhance learning through AI tools like ChatGPT. Within anatomy education, Totlis et al. (2023) report that ChatGPT offers personalized explanations and feedback, which aid in creating educational content and enhance accessibility through language translation. Their study found that 85% of students felt more engaged and better understood complex anatomical concepts when using AI‐driven tools. Similar results have been found in different AI platforms, like Google Bard and regions outside the U.S., such as Turkey (Ilgaz and Çelik 2023). These AI tools can effectively tailor educational experiences to students' individual needs, thereby optimizing learning outcomes and better preparing them for clinical practice (Totlis et al. 2023; Ilgaz and Çelik 2023). Furthermore, Lee (2024) noted that ChatGPT could be integrated with interactive simulations, such as virtual histology programs, to provide real‐time explanations of anatomical structures. This interactive approach has shown promise in enhancing comprehension and making anatomy education more engaging.

4.1.3. AI in Anatomical Imaging and Simulations

Another exciting application of AI in anatomy education is its use in enhancing anatomical illustrations and simulations. AI‐powered text‐to‐image generators can create highly accurate and detailed anatomical diagrams, providing valuable educational aids. A comparative study by Noel (2023) demonstrated that AI‐generated illustrations were as effective as traditional illustrations in helping students understand complex anatomical concepts. Moreover, these AI tools can quickly generate custom diagrams tailored to specific educational needs, providing an invaluable resource for educators. A recent study by Al‐Khater (2024) evaluated AI chatbots in identifying radiographic images. While Claude excels in providing detailed contextual information for radiographic images, ChatGPT's accuracy and adaptability make it a versatile tool for general anatomy education, highlighting the potential for these platforms to complement each other. This suggests that different AI platforms may have complementary roles and highlights the potential of these specific AI tools to enhance imaging‐based anatomy education.

In addition, ChatGPT‐4 has shown exceptional promise in creating realistic anatomical scenarios and simulations. According to Mavrych et al. (2024), ChatGPT‐4 generated high‐quality clinical scenarios and MCQs for anatomy topics with an average score of 4.43 out of 5 in expert evaluations. These AI‐generated simulations mimic realistic clinical contexts and allow students to interact with complex anatomical concepts. Such advancements position AI as a critical supplement to traditional anatomical teaching methods, particularly in areas requiring visualization and application.

The modernization of anatomy education through AI also coincides with developments in parallel technology such as virtual reality (VR) and augmented reality (AR). A study by Lee and Son (2023) found that approximately 90% of students using VR for anatomy education reported a significant improvement in their understanding of anatomical structures compared to traditional learning methods. AI can enhance traditional anatomy education methods by offering interactive and immersive learning experiences if it works with VR. For example, AI tools can analyze imaging data to create detailed 3D models of anatomical structures, allowing students to visualize and interact with anatomy in ways that traditional methods do not offer (Lazarus et al. 2024). This shows yet another way to provide personalized learning programs powered by AI that adapt to the unique learning needs of each student, delivering content and exercises tailored to one's specific strengths and weaknesses (Abdellatif et al. 2022). Collins et al. (2024) also highlighted the capability of AnatomyGPT to generate 3D anatomical models, making it a significant innovation in anatomical simulations. By providing students with visual tools and interactive features, AnatomyGPT bridges the gap between theoretical and practical anatomy education. Building on this, Chytas et al. (2023) reviewed virtual dissection tables, finding them valuable in enhancing anatomy curricula. These tables enable repeated dissections and detailed visualization of complex anatomical structures, making them vital to blended‐learning approaches in anatomy education. By integrating clinical contexts, virtual dissection tables provide an interactive supplement to cadaveric study, bridging theoretical and practical learning in a way that supports the growing reliance on technology in medical education.

4.1.4. AI in Assessment and Feedback

AI's integration into assessment and feedback processes presents significant advancements in anatomy education, offering a more objective, efficient, and personalized approach. AI‐driven platforms can analyze student performance in real time, especially in formative assessments, providing immediate and constructive feedback to enhance student understanding (Lazarus et al. 2024). Collins et al. (2024) showed that AnatomyGPT can analyze student performance on standardized anatomy exams and provide referenced explanations. This capability supports more effective learning by helping students identify and address their weaknesses. Furthermore, Kung et al. (2023) demonstrated that students using AI‐powered tutoring systems showed a 20% improvement in test scores compared to those using traditional study methods, showcasing the transformative potential of AI in boosting learning outcomes.

A significant benefit of AI in assessments is its capacity to deliver consistent and impartial evaluations, which can be challenging with human assessors. Traditional assessments are susceptible to innate biases—cultural, gender, or subjectivity—resulting in variability in how anatomical knowledge and skills are evaluated (Patra et al. 2023). While traditional MCQ‐based exams are widely considered objective, they still contain biases in question selection, difficulty level, and accessibility. AI‐based adaptive assessment tools provide a more equitable evaluation by dynamically adjusting question difficulty, tracking individual learning progress, and providing real‐time feedback tailored to student needs (Patra et al. 2023). Unlike fixed‐question MCQs, AI‐powered platforms such as Anatomage and Kenhub ensure personalized assessment, reducing test‐taking ability, cognitive load, and examiner subjectivity biases. AI‐based assessment tools eliminate such biases by automatically adjusting difficulty levels and ensuring uniformity in testing, thereby promoting standardization and validity in student evaluations. This fosters a more equitable environment, where students are evaluated on their actual knowledge and skills rather than the subjective judgments of different examiners.

AI offers considerable time‐saving benefits for educators by automating grading and feedback processes. Chan and Zary (2019) found that AI‐based tools improved grading accuracy by 25% and efficiency by 35%, reducing the administrative workload and allowing educators to dedicate more time to instruction. AI systems such as ChatGPT can also generate exam questions tailored to individual student needs and difficulty levels, easing the burden on teachers while offering personalized exam preparation (Leng 2024). This ability to generate customized questions on demand, while maintaining reliability in content, underscores AI's growing role in anatomy education. Additionally, using augmented reality (AR) platforms integrated with AI has shown promising results in enhancing student engagement and learning outcomes. Fajrianti et al. (2022) demonstrated that students using an AR platform for anatomy quizzes, which adapts to different difficulty levels, achieved higher comprehension scores than traditional paper‐based tests. The AR mode's average scores—94.46 for easy, 90 for medium, and 83 for difficult levels—outperformed the non‐AR mode, suggesting that AI‐driven AR platforms provide a more interactive and effective learning experience. This technological advancement makes learning more engaging and improves students' understanding of complex anatomical concepts.

AI's integration into assessment and feedback in anatomy education improves grading efficiency and accuracy and enhances the learning experience through personalized, objective, and adaptive evaluations. This marks a shift toward more equitable and engaging educational practices, where students can benefit from real‐time, data‐driven feedback that aligns with their learning needs and progress.

4.1.5. Advancing AI for Enhanced Learning and Clinical Integration

AI continues to reshape anatomy education, and its future lies in refining its capabilities for more immersive and practical learning. Future advancements should focus on expanding AI‐driven adaptive learning, where real‐time performance tracking and intelligent feedback systems optimize educational experiences (Chan and Zary 2019; Leng 2024). AI‐powered virtual dissection tables and interactive simulations should be further developed to promote deeper engagement and reinforce anatomical comprehension through dynamic, student‐specific guidance (Chytas et al. 2023; Collins et al. 2024).

AI's growing role in interdisciplinary medical education will be a key area of progress. Integrating AI into surgical training, radiology, and pathology will create a seamless transition from anatomy fundamentals to clinical application (Mazhar et al. 2024; Noel 2023). Future AI platforms should offer clinical decision‐making support, allowing students to correlate anatomical knowledge with real‐world patient cases, bridging the gap between foundational learning and professional practice.

Collaborative efforts between educators, clinicians, and AI developers will be essential to ensuring AI tools align with real‐world medical education needs. Advancements in AI require continued investment in evidence‐based AI research, refining machine learning models to simulate clinical environments better, and enhancing problem‐solving skills. AI‐driven assessments should evolve to evaluate knowledge retention, critical thinking, and applied decision‐making.

As AI technology advances, its integration into anatomy education must be approached strategically to ensure it complements rather than replaces traditional methodologies. A forward‐looking approach will allow AI to serve as a transformative tool, enhancing accessibility, engagement, and the overall quality of medical education while preparing students for the demands of modern healthcare.

4.2. Challenges and Ethical Considerations in AI‐Driven Anatomy Education

While AI presents numerous benefits for anatomy education, its integration also introduces significant challenges and limitations. One key concern is the over‐reliance on technology, which can diminish the importance of traditional teaching methods that emphasize human interaction, clinical experience, and the development of critical thinking skills. However, critics who equate AI to passive automation often overlook its potential as a catalyst for deeper learning. This critique parallels how a model student in a study group serves not as a crutch but as a dynamic facilitator for expert conversations and shared learning. Similarly, AI systems can enhance human cognition by introducing novel perspectives, stimulating creativity, and enriching discourse among learners (Shin et al. 2023). For example, Shin et al. (2023) demonstrated that superhuman AI in strategic games encouraged professionals to adopt innovative strategies, enhancing their decision‐making capabilities beyond conventional norms. This synergistic relationship, where AI acts as both a knowledge repository and a creative collaborator, is mirrored in anatomy education when AI tools like ChatGPT are used for generating tailored feedback or guiding exploratory problem‐solving sessions.

Moreover, the integration of AI in collaborative models has shown practical value in healthcare settings. Wan et al. (2024) highlighted how a hybrid model combining AI and human oversight in outpatient care improved communication outcomes, empathy, and efficiency without diminishing human agency. AI tools can also assist physicians in crafting more thoughtful and supportive communications, ultimately fostering stronger patient‐provider relationships (Ayers et al. 2023). Beyond merely mimicking human interaction, these tools serve as a training resource, helping healthcare professionals improve their own communication skills by providing real‐time feedback and high‐quality examples of empathetic responses (Ayers et al. 2023). Applying this analogy to education, AI's role as a “colleague” might involve enhancing group dynamics, promoting adaptive learning pathways, and supporting learners in mastering complex material.

Critically, these AI‐enabled dynamics require thoughtful implementation. Educators must guide students on leveraging AI as a complement rather than a substitute for human expertise, emphasizing its role in fostering inquiry, dialogue, and shared problem‐solving rather than rote automation. Overall, the collaboration between humans and AI demonstrates a powerful symbiosis. AI is not merely a tool but a dynamic partner that enriches human creativity, decision‐making, and communication. Whether it is supporting healthcare providers in their daily interactions, inspiring innovative strategies in high‐stakes contexts, or leveling the playing field in education, AI has proven to be a valuable resource in amplifying human potential. Thus, AI in anatomy education should be viewed not as replacing traditional methods but as amplifying the intellectual engagement characteristic of collaborative learning environments Figures 1 and 2.

4.2.1. Ethical Considerations in AI Integration

One of the central concerns surrounding the use of AI in anatomy education is its potential to diminish the human elements of medical training. As demonstrated by Collins et al. (2024) and Arun et al. (2024), the customization of AI models highlights the importance of ensuring ethical oversight in AI's development and deployment. These studies emphasize the need to carefully curate knowledge bases to prevent biases and inaccuracies in AI‐driven education. Jaffer et al. (2022) argue that AI‐generated simulations in cadaveric dissection rooms may eventually replace human donors, leading to the loss of critical experiences such as empathy, respect for the human body, and a hands‐on understanding of anatomy. Traditional dissection fosters these values, which are crucial to the ethical formation of future medical professionals. Over‐reliance on AI technology could erode these elements, making it essential to ensure that AI enhances, rather than replaces, traditional teaching methods.

4.2.2. Data Bias and Curation Challenges

AI in anatomy education relies heavily on the quality and representativeness of the data it is trained on. Poorly curated datasets can lead to biased simulations and learning experiences, exacerbating healthcare disparities. Jaffer et al. (2022) and Khalil and Roe (2023) highlight the risks of AI systems perpetuating biases related to race, gender, and socioeconomic status, especially if these factors are not well represented in the training data. Abdellatif et al. (2022) also underscore the lack of high‐quality, randomized controlled trials to back the use of AI in anatomy education. Without proper evidence and backward‐designed datasets, the effectiveness and fairness of AI tools remain in question, making data curation a critical area of concern.

4.2.3. Accuracy and Reliability of AI Tools

A significant limitation of AI in anatomy education is the potential for disseminating inaccurate or outdated information. Leng (2024) emphasizes that tools like ChatGPT, while helpful in generating educational content, achieved only 72.5% accuracy in anatomical knowledge, with even lower reliability in more complex areas like vessel anatomy. Given the need for high precision in medical education, these errors could lead to significant consequences in patient care if not adequately mitigated. Therefore, ensuring the accuracy and reliability of AI tools requires robust oversight, clear guidelines, and continuous updates, preferably overseen by educational authorities and institutions.

Additionally, some limitations will require expert oversight for the foreseeable future to ensure that AI‐generated educational content is accurate and reliable. While valuable, generative AI responses may lack the human judgment and expertise needed to assess the nuances of complex medical education. If content is not carefully curated and validated, inaccurate or outdated information can be disseminated (Totlis et al. 2023). Human oversight and ethical considerations are essential to prevent these potential pitfalls in AI implementation.

However, ensuring the successful integration of AI into anatomy education involves more than just content validation. It also requires access to high‐quality, representative data sets and comprehensive teacher training. These requirements can be resource‐intensive and pose challenges for institutions, particularly those with limited financial and technological resources (Abdellatif et al. 2022). The resource demands further highlight the importance of high‐level planning and resource allocation, underscoring that educators and developers must actively address these challenges to fully maximize AI's benefits while minimizing potential drawbacks.

4.2.4. Impact on Critical Thinking and Student Engagement

A significant concern with integrating AI into education is the potential reduction in student engagement and critical thinking. Over‐reliance on AI‐generated responses for assignments and learning tasks could lead students to disengage from the material, stifling creativity and essential problem‐solving skills. This risk highlights the importance of designing AI‐based systems that encourage active learning rather than passive consumption of information.

AI presents an opportunity to transform how students engage with material, particularly through active learning strategies. For instance, Explainable Active Learning (XAL) systems enable learners to interact dynamically with AI, validating its reasoning and offering feedback, thereby fostering critical thinking and deeper engagement with content. This interactive process not only improves student understanding but also enhances their trust and confidence in the learning experience (Ghai et al. 2020). Similarly, mixed‐reality AI systems, such as Intelligent Science Stations, combine hands‐on tasks with guided discovery. These systems provide real‐time feedback during physical experimentation, significantly improving problem‐solving abilities and conceptual understanding compared to traditional methods (Yannier et al. 2020).

AI's ability to personalize learning environments can further improve engagement. Generative AI tools dynamically adapt instructional content to match individual learning styles, ensuring that students receive tailored support as they progress. This personalized approach fosters a sense of ownership and active participation, which is critical for sustained engagement and deeper learning (Nguyen et al. 2024). Additionally, AI‐powered tutoring systems and chatbots provide immediate, targeted feedback, helping students refine their understanding and skills in real time (Nguyen et al. 2024).

By leveraging these capabilities, educators can rethink traditional assessment methods to emphasize process‐oriented learning over rote memorization. For example, reflective journaling facilitated by AI and real‐time feedback on live performance tasks can help educators assess critical thinking and application skills more effectively. These innovations encourage active learning and equip students with the knowledge and problem‐solving abilities necessary for their professional development.

4.2.5. Visual Learning Limitations

Anatomy is a highly visual field, and current AI systems face significant challenges in effectively processing and generating anatomical images. Leng (2024) points out that tools like ChatGPT struggle with tasks that require visual comprehension, which is crucial for disciplines such as gross anatomy, where understanding spatial relationships is essential. Without strong visual learning capabilities, AI's utility in anatomy education is limited, and students may miss out on the important visual aspects of anatomy that are critical for clinical practice.

4.2.6. Algorithmic Bias and Discrimination

AI systems in anatomy education can perpetuate biases if trained on non‐representative datasets, leading to disparities in learning outcomes (Al‐Khater 2024). Bias may manifest in content generation, assessment methods, and feedback mechanisms, potentially disadvantaging underrepresented groups.

One clear example from Al‐Khater (2024) demonstrated that Gemini, compared to ChatGPT and Claude, produced incorrect and inconsistent anatomical descriptions, particularly regarding the wrist joint. This discrepancy suggests that the AI model was trained on incomplete or unbalanced datasets, leading to inaccuracies in educational materials. Similarly, Khalil and Roe (2023) found that AI‐driven emotion‐tracking tools misinterpreted engagement levels among students from different ethnic backgrounds. This resulted in biased performance assessments, where particular groups were unfairly categorized as disengaged due to differences in facial expressions and learning behaviors.

These biases in AI‐generated anatomy content can reinforce disparities in medical education. Students relying on inaccurate anatomical information may develop misconceptions that could impact their clinical decision‐making. Moreover, engagement tracking biases may lead to unfair evaluations, affecting academic progression for students from underrepresented groups.

To mitigate these biases, AI models must be trained on diverse datasets, undergo regular validation, and integrate human oversight to ensure accuracy and inclusivity (Al‐Khater 2024). Institutions should conduct periodic audits of AI‐generated content and prioritize AI platforms with demonstrated reliability to prevent the reinforcement of educational inequities. A more inclusive approach to AI development in anatomy education ensures that all students receive accurate, equitable, and unbiased educational experiences.

AI systems, including those used in medical education, are susceptible to perpetuating algorithmic biases, particularly when the training data is not diverse or inclusive. Leng (2024) warns that models like ChatGPT can reproduce and even amplify biases related to gender, race, and other demographic factors. This could negatively affect the learning experience for students and may even influence the biases they carry into clinical practice. For instance, Al‐Khater (2024) observed that while platforms like ChatGPT and Claude excel in accuracy, others like Gemini produce incorrect and inconsistent anatomical descriptions of structures like the wrist joint, raising concerns about data integrity and inclusivity. The inaccuracies observed in Gemini's anatomical descriptions, such as misidentifying joint structures, could mislead learners and diminish trust in AI tools. Addressing this issue requires concerted efforts to develop transparent AI systems trained on diverse and representative datasets that reflect the complexity and variety of real‐world patient populations.

4.2.7. Limitations: Reliability and Accuracy of AI in Anatomy Education

A significant limitation of AI in anatomy education is its potential for inaccuracies and inconsistencies in generated content (AI hallucinations). While AI‐powered tools such as ChatGPT and AnatomyGPT have demonstrated high accuracy rates in anatomy assessments (Collins et al. 2024; Al‐Khater 2024), studies have shown that AI models can still produce incorrect or misleading information, mainly when dealing with complex anatomical structures or nuanced medical concepts (Leng 2024). Leng (2024) highlights that AI tools like ChatGPT struggle with tasks requiring high visual comprehension, which is crucial in anatomy education, where understanding spatial relationships between structures is essential.

Moreover, AI models lack the critical thinking, clinical judgment, and contextual awareness that human educators provide (Lazarus et al. 2024). The reliance on large datasets, which may contain inherent biases or outdated information, further exacerbates this limitation (Jaffer et al. 2022; Khalil and Roe 2023). For instance, Khalil and Roe (2023) emphasize the risks of AI perpetuating biases related to race, gender, and anatomical variations, which could negatively influence students' learning and clinical applications. Similarly, Jaffer et al. (2022) argue that while AI‐generated simulations are valuable, they cannot fully replicate cadaveric dissection's ethical and hands‐on experiences.

Some studies also caution against AI over‐reliance, as it may weaken students' self‐assessment and critical thinking skills (Tolsgaard et al. 2023). Mavrych et al. (2024) compared multiple AI models, finding that while ChatGPT‐4 had a relatively high accuracy rate (60.5%), other models, such as Bard and PaLM, performed inconsistently in medical education applications, raising concerns about reliability. Al‐Khater (2024) further demonstrated that some AI platforms, like Gemini, produced incorrect and inconsistent anatomical descriptions, misidentifying structures such as the wrist joint, which could mislead learners and undermine their trust in AI‐based tools.

Before integrating AI‐generated educational content into medical curricula, expert review, continuous refinement, and validation are necessary to ensure accuracy and reliability (Collins et al. 2024; Masters 2023). Educators should complement AI tools with traditional learning approaches to ensure accuracy, depth, and clinical applicability in anatomy education. Future advancements should focus on developing more reliable AI systems with enhanced visual learning capabilities and built‐in quality control mechanisms (Bankar et al. 2024). A balanced approach to blending AI innovations with human oversight remains essential to preserving the integrity and effectiveness of anatomy education.

4.3. Practical Implications of Using AI in Anatomy Education

To address uncertainties surrounding AI, institutions must consider the diversity of AI developers, account for anatomical variations in AI‐deployed systems, and raise educators' awareness of both the benefits and limitations of AI. Further, establishing “AI‐free” time in courses can ensure balance, while AI can be leveraged to enhance human capabilities rather than replace them (Bankar et al. 2024).

The findings by Mavrych et al. (2024) and Al‐Khater (2024) reinforce the importance of integrating AI tools into anatomy education alongside traditional methods. While platforms like ChatGPT‐4 demonstrate significant potential in enhancing learning outcomes, they are not mature enough to replace educators entirely. Instead, these tools should complement hands‐on learning experiences. For example, Mavrych et al. (2024) advocate for leveraging AI to generate clinically relevant educational content while maintaining human oversight to ensure accuracy and relevance.

Furthermore, Al‐Khater (2024) suggests that institutions should prioritize high‐performing platforms like ChatGPT and Claude while cautioning against less reliable tools like Gemini. These findings highlight the need to carefully select and integrate AI platforms to maximize their benefits while addressing their limitations.

Chytas et al. (2023) and Collins et al. (2024) suggest that blending traditional methods with AI‐driven tools, such as virtual dissection tables and customized AI models, can enhance anatomy education while maintaining essential human elements. This hybrid approach ensures that technology complements rather than replaces hands‐on learning. By integrating a range of AI applications into medical education, anatomy teachers have embraced modern technologies, creating a “smart teaching system” that bridges the gap between traditional methods and the digital generation of students (Bankar et al. 2024).

5. Conclusion

In conclusion, the integration of artificial intelligence (AI) into anatomy education represents a significant evolution in medical training. It addresses the challenges of traditional methods while aligning with the learning preferences of today's digital‐native students. AI offers innovative tools that enhance personalized learning, engagement, and assessment processes, transforming how anatomical knowledge is delivered and understood. By providing tailored feedback, interactive simulations, and efficient assessment mechanisms, AI has the potential to improve educational outcomes and better prepare students for clinical practice; it's the equivalent of having an experienced clinical consultant colleague on hand for all learning and interactions when designed and used appropriately.

However, the adoption of AI in anatomy education has its challenges. Concerns regarding the diminished role of human interaction, the potential for bias in AI systems, and the risk of undermining critical thinking skills must be carefully managed. Educators must strive to balance the use of AI with traditional teaching methods, ensuring that the human elements of empathy, ethical understanding, and hands‐on experience remain central to medical training.

As the field of anatomy education continues to evolve, it is crucial to establish robust oversight, data quality standards, and ethical guidelines to ensure that AI enhances the educational experience rather than detracts from it. By addressing these challenges, medical educators can harness the full potential of AI, paving the way for a more effective, engaging, and equitable approach to anatomy education that ultimately benefits future healthcare practitioners and their patients.

Disclosure

The authors have nothing to report.

Ethics Statement

The authors have nothing to report.

Supporting information

Data S1. PRISMA flowchart showing breakdown of identification and screening in the literature search.

CA-38-552-s001.docx (40KB, docx)

Acknowledgments

The authors have nothing to report.

Funding: The authors received no specific funding for this work.

Data Availability Statement

The authors have nothing to report.

References

  1. Abdellatif, H. , Al Mushaiqri M., Albalushi H., Al‐Zaabi A. A., Roychoudhury S., and Das S.. 2022. “Teaching, Learning and Assessing Anatomy With Artificial Intelligence: The Road to a Better Future.” International Journal of Environmental Research and Public Health 19, no. 21: 14209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Al‐Khater, K. M. K. 2024. “Comparative Assessment of Three AI Platforms in Answering USMLE Step 1 Anatomy Questions or Identifying Anatomical Structures on Radiographs.” Clinical Anatomy 38, no. 2: 186–199. [DOI] [PubMed] [Google Scholar]
  3. Arun, G. , Perumal V., Urias F. P. J. B., et al. 2024. “ChatGPT Versus a Customized AI Chatbot (Anatbuddy) for Anatomy Education: A Comparative Pilot Study.” Anatomical Sciences Education 17, no. 7: 1396–1405. 10.1002/ase.2502. [DOI] [PubMed] [Google Scholar]
  4. Asghar, A. , Patra A., and Ravi K. S.. 2022. “The Potential Scope of a Humanoid Robot in Anatomy Education: A Review of a Unique Proposal.” Surgical and Radiologic Anatomy 44, no. 10: 1309–1317. [DOI] [PubMed] [Google Scholar]
  5. Ayers, J. W. , Poliak A., Dredze M., et al. 2023. “Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.” JAMA Internal Medicine 183, no. 6: 589–596. 10.1001/jamainternmed.2023.1838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bankar, M. N. , Bankar N. J., Gajbe U., et al. 2024. “A Narrative Literature Review on New Technologies for Teaching Anatomy.” International Journal of Academic Medicine 10, no. 2: 47–55. [Google Scholar]
  7. Castellano, M. S. , Contreras‐McKay I., Neyem A., et al. 2024. “Empowering Human Anatomy Education Through Gamification and Artificial Intelligence: An Innovative Approach to Knowledge Appropriation.” Clinical Anatomy 37, no. 1: 12–24. 10.1002/ca.24074. [DOI] [PubMed] [Google Scholar]
  8. Chan, K. S. , and Zary N.. 2019. “Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review.” JMIR Medical Education 5, no. 1: e13930. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chan, L. K. , and Pawlina W.. 2019. “Artificial Intelligence or Natural Stupidity? Deep Learning or Superficial Teaching?” Anatomical Sciences Education. 10.1002/ase.1936. [DOI] [PubMed] [Google Scholar]
  10. Chen, M. , and Decary M.. 2020. “Artificial Intelligence in Healthcare: An Essential Guide for Health Leaders.” Healthcare Management Forum 33, no. 1: 10–18. 10.1177/0840470419873123. [DOI] [PubMed] [Google Scholar]
  11. Chytas, D. , Salmas M., Noussios G., et al. 2023. “Do Virtual Dissection Tables Add Benefit to Cadaver‐Based Anatomy Education? An Evaluation.” Morphologie: Bulletin de l'Association des Anatomistes 107, no. 356: 1–5. 10.1016/j.morpho.2022.01.002. [DOI] [PubMed] [Google Scholar]
  12. Collins, B. R. , Black E. W., and Rarey K. E.. 2024. “Introducing AnatomyGPT: A Customized Artificial Intelligence Application for Anatomical Sciences Education.” Clinical Anatomy 37, no. 6: 661–669. 10.1002/ca.24178. [DOI] [PubMed] [Google Scholar]
  13. Cornwall, J. , Hildebrandt S., Champney T. H., and Goodman K.. 2024. “Ethical Concerns Surrounding Artificial Intelligence in Anatomy Education: Should AI Human Body Simulations Replace Donors in the Dissection Room?” Anatomical Sciences Education 17, no. 5: 937–943. [DOI] [PubMed] [Google Scholar]
  14. Eysenbach, G. 2023. “The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers.” JMIR Medical Education 9, no. 1: e46885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fajrianti, E. D. , Sukaridhoto S., Rasyid M. U. H. A., et al. 2022. “Application of Augmented Intelligence Technology With Human Body Tracking for Human Anatomy Education.” IJIET: International Journal of Information and Education Technology 12, no. 6: 476–484. [Google Scholar]
  16. Ghai, B. , Liao Q. V., Zhang Y., Bellamy R. K. E., and Mueller K.. 2020. “Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers.” Proceedings of the ACM on Human‐Computer Interaction 4, no. 3: 1–28. 10.1145/3432934. [DOI] [Google Scholar]
  17. Guimarães, B. , Dourado L., Tsisar S., Diniz J. M., Madeira M. D., and Ferreira M. A.. 2017. “Rethinking Anatomy: How to Overcome Challenges of Medical Education's Evolution.” Acta Medica Portuguesa 30, no. 2: 134–140. [DOI] [PubMed] [Google Scholar]
  18. Han, E. R. , Yeo S., Kim M. J., Lee Y. H., Park K. H., and Roh H.. 2019. “Medical Education Trends for Future Physicians in the Era of Advanced Technology and Artificial Intelligence: An Integrative Review.” BMC Medical Education 19: 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ilgaz, H. B. , and Çelik Z.. 2023. “The Significance of Artificial Intelligence Platforms in Anatomy Education: An Experience With ChatGPT and Google Bard.” Cureus 15, no. 9: e45301. 10.7759/cureus.45301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Iwanaga, J. , Loukas M., Dumont A. S., and Tubbs R. S.. 2021. “A Review of Anatomy Education During and After the COVID‐19 Pandemic: Revisiting Traditional and Modern Methods to Achieve Future Innovation.” Clinical Anatomy 34, no. 1: 108–114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Iwanaga, J. , Muo E. C., Tabira Y., et al. 2023. “Who Really Needs a Metaverse in Anatomy Education? A Review With Preliminary Survey Results.” Clinical Anatomy 36, no. 1: 77–82. [DOI] [PubMed] [Google Scholar]
  22. Jacobs, E. , Wainman B., and Bowness J.. 2023. “Applying Artificial Intelligence to the Use of Ultrasound as an Educational Tool: A Focus on Ultrasound‐Guided Regional Anesthesia.” Anatomical Sciences Education 17, no. 5: 919–925. 10.1002/ase.2266. [DOI] [PubMed] [Google Scholar]
  23. Jaffer, U. , Ahmed M., and Singh S.. 2022. “The Potential Scope of a Humanoid Robot in Anatomy Education: A Review of a Unique Proposal.” Anatomical Sciences Education 15, no. 3: 352–358. 10.1002/ase.2141.33783136 [DOI] [Google Scholar]
  24. Khalil, M. K. , and Roe K.. 2023. “The Use of Artificial Intelligence to Detect Students' Sentiments and Emotions in Gross Anatomy Reflections.” Journal of Medical Education and Curriculum Development 10, no. 2: 112–125. 10.1177/2382120523101456. [DOI] [Google Scholar]
  25. Kung, T. H. , Cheatham M., Medenilla A., et al. 2023. “Performance of ChatGPT on USMLE: Potential for AI‐Assisted Medical Education Using Large Language Models.” PLOS Digital Health 2, no. 2: e0000198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lazarus, M. D. , Rosario B. L., and Williams J. D.. 2019. “Rethinking Anatomy: How to Overcome Challenges of Medical Education's Evolution.” Acta Médica Portuguesa 30, no. 2: 123–135. 10.20344/amp.10832. [DOI] [PubMed] [Google Scholar]
  27. Lazarus, M. D. , Truong M., Douglas P., and Selwyn N.. 2024. “Artificial Intelligence and Clinical Anatomical Education: Promises and Perils.” Anatomical Sciences Education 17, no. 2: 249–262. [DOI] [PubMed] [Google Scholar]
  28. Lee, H. 2024. “The Rise of ChatGPT: Exploring Its Potential in Medical Education.” Anatomical Sciences Education 17, no. 5: 926–931. [DOI] [PubMed] [Google Scholar]
  29. Lee, J. S. , and Son H. K.. 2023. “Evaluation of a Virtual Reality Simulation to Improve Problem‐Based Learning for Neurologic Examination in Nursing Students.” Iranian Journal of Public Health 52, no. 10: 2128–2137. 10.18502/ijph.v52i10.13851. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Leng, L. 2024. “Challenge, Integration, and Change: ChatGPT and Future Anatomical Education.” Medical Education Online 29, no. 1: 2304973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Li, Y. S. , Lam C. S. N., and See C.. 2021. “Using a Machine Learning Architecture to Create an AI‐Powered Chatbot for Anatomy Education.” Medical Science Educator 31, no. 6: 1729–1730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lindqwister, A. L. , Hassanpour S., Lewis P. J., and Sin J. M.. 2021. “AI‐RADS: An Artificial Intelligence Curriculum for Residents.” Academic Radiology 28, no. 12: 1810–1816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Liu, S. , Li J., Zhang H., et al. 2024. “Development and Implementation of Digital Pedagogical Support Systems in the Context of Educational Equity.” Humanities and Social Sciences Communications 11: 1084. 10.1057/s41599-024-03616-y. [DOI] [Google Scholar]
  34. Masters, K. 2023. “Ethical Use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158.” Medical Teacher 45, no. 6: 574–584. [DOI] [PubMed] [Google Scholar]
  35. Mavrych, V. , Ganguly P., and Bolgova O.. 2024. “Using Large Language Models (ChatGPT, Copilot, PaLM, Bard, and Gemini) in Gross Anatomy Course: Comparative Analysis.” Clinical Anatomy 38, no. 2: 200–210. [DOI] [PubMed] [Google Scholar]
  36. Mazhar, M. A. , Qazi S., and Sarwa S.. 2024. “The Future of Anatomy Education: Simulation‐Based and AI‐Based Learning.” Journal of Clinical Nursing 33, no. 6: 2357–2358. 10.1111/jocn.17074. [DOI] [PubMed] [Google Scholar]
  37. Miltykh, I. , Kafarov E. S., Covantsev S., Dadashev A. S., Skarlis A. A., and Zenin O. K.. 2023. “A New Dimension in Medical Education: Virtual Reality in Anatomy During COVID‐19 Pandemic.” Clinical Anatomy 36, no. 7: 1007–1015. 10.1002/ca.24098. [DOI] [PubMed] [Google Scholar]
  38. Moshirfar, M. , Altaf A. W., Stoakes I. M., Tuttle J. J., and Hoopes P. C.. 2023. “Artificial Intelligence in Ophthalmology: A Comparative Analysis of GPT‐3.5, GPT‐4, and Human Expertise in Answering.” StatPearls Questions 15, no. 6: e40822. 10.7759/cureus.40822.. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Nguyen, A. , Kremantzis M., Essien A., Petrounias I., and Hosseini S.. 2024. “Enhancing Student Engagement Through Artificial Intelligence (AI): Understanding the Basics, Opportunities, and Challenges.” Journal of University Teaching and Learning Practice 21, no. 6: 1–13. [Google Scholar]
  40. Noel, G. P. J. C. 2023. “Evaluating AI‐Powered Text‐To‐Image Generators for Anatomical Illustration: A Comparative Study.” Anatomical Sciences Education 17, no. 5: 979–983. 10.1002/ase.2336. [DOI] [PubMed] [Google Scholar]
  41. Ottone, N. E. , Vargas C. A., and Herrera J. L.. 2021. “Empowering Human Anatomy Education Through Gamification and Artificial Intelligence: An Innovative Approach to Knowledge Appropriation.” Journal of Anatomy and Physiology 17, no. 4: 201–215. 10.1002/jap.1212. [DOI] [PubMed] [Google Scholar]
  42. Patra, A. , Pushpa N. B., and Ravi K. S.. 2023. “Visualization in Anatomy Education.” In Biomedical Visualisation: Volume 15–Visualisation in Teaching of Biomedical and Clinical Subjects: Anatomy, Advanced Microscopy and Radiology, 171–186. Springer International Publishing. [Google Scholar]
  43. Rechowicz, K. J. , and Elzie C. A.. 2024. “The Use of Artificial Intelligence to Detect students' Sentiments and Emotions in Gross Anatomy Reflections.” Anatomical Sciences Education 17, no. 5: 954–966. [DOI] [PubMed] [Google Scholar]
  44. Richardson, M. L. , Garwood E. R., Lee Y., et al. 2021. “Noninterpretive Uses of Artificial Intelligence in Radiology.” Academic Radiology 28, no. 9: 1225–1235. [DOI] [PubMed] [Google Scholar]
  45. Rizzolo, L. J. , Rando W. C., O'Brien M. K., Haims A. H., Abrahams J. J., and Stewart W. B.. 2010. “Design, Implementation, and Evaluation of an Innovative Anatomy Course.” Anatomical Sciences Education 3, no. 3: 109–120. [DOI] [PubMed] [Google Scholar]
  46. Rodrigues, L. , Toda A. M., Oliveira W., Palomino P. T., Vassileva J., and Isotani S.. 2022. “Automating Gamification Personalization to the User and Beyond.” IEEE Transactions on Learning Technologies 15, no. 2: 199–212. [Google Scholar]
  47. Shaikh, A. A. , Kumar A., Jani K., Mitra S., García‐Tadeo D. A., and Devarajan A.. 2022. “The Role of Machine Learning and Artificial Intelligence for Making a Digital Classroom and Its Sustainable Impact on Education During COVID‐19.” Materials Today Proceedings 56: 3211–3215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Shin, M. , Kim J., Van Opheusden B., and Griffiths T. L.. 2023. “Superhuman Artificial Intelligence Can Improve Human Decision‐Making by Increasing Novelty.” Proceedings of the National Academy of Sciences 120, no. 12: e2214840120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sugand, K. , Abrahams P., and Khurana A.. 2010. “The Anatomy of Anatomy: A Review for Its Modernization.” Anatomical Sciences Education 3, no. 2: 83–93. [DOI] [PubMed] [Google Scholar]
  50. Taranikanti, V. , and Davidson C. J.. 2023. “Metacognition Through an Iterative Anatomy AI Chatbot: An Innovative Playing Field for Educating the Future Generation of Medical Students.” Anatomia 2, no. 3: 271–281. [Google Scholar]
  51. Tolsgaard, M. G. , Pusic M. V., Sebok‐Syer S. S., et al. 2023. “The Fundamentals of Artificial Intelligence in Medical Education Research: AMEE Guide No. 156.” Medical Teacher 45, no. 6: 565–573. [DOI] [PubMed] [Google Scholar]
  52. Totlis, T. , Natsis K., Filos D., et al. 2023. “The Potential Role of ChatGPT and Artificial Intelligence in Anatomy Education: A Conversation With ChatGPT.” Surgical and Radiologic Anatomy 45, no. 10: 1321–1329. 10.1007/s00276-023-03229-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Turney, B. W. 2007. “Anatomy in a Modern Medical Curriculum.” Annals of the Royal College of Surgeons of England 89, no. 2: 104–107. 10.1308/003588407X168244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Urh, M. , Vukovic G., and Jereb E.. 2015. “The Model for Introduction of Gamification Into e‐Learning in Higher Education.” Procedia‐Social and Behavioral Sciences 197: 388–397. [Google Scholar]
  55. Vázquez, R. , Riesco J. M., and Carretero J.. 2005. “Reflections and Challenges in the Teaching of Human Anatomy at the Beginning of the 21st Century.” European Journal of Anatomy 9, no. 2: 111. [Google Scholar]
  56. Wan, P. , Huang Z., Tang W., et al. 2024. “Outpatient Reception via Collaboration Between Nurses and a Large Language Model: A Randomized Controlled Trial.” Nature Medicine 30, no. 10: 2878–2885. 10.1038/s41591-024-03148-7.. [DOI] [PubMed] [Google Scholar]
  57. Williams, A. , Sun Z., and Vaccarezza M.. 2025. “Comparison of Augmented Reality With Other Teaching Methods in Learning Anatomy: A Systematic Review.” Clinical Anatomy 38, no. 2: 168–185. 10.1002/ca.24234. [DOI] [PubMed] [Google Scholar]
  58. Wittich, C. M. , Pawlina W., Drake R. L., et al. 2013. “Validation of a Method for Measuring Medical Students' Critical Reflections on Professionalism in Gross Anatomy.” Anatomical Sciences Education 6, no. 4: 232–238. 10.1002/ase.1329. [DOI] [PubMed] [Google Scholar]
  59. Yang, S. J. , Ogata H., Matsui T., and Chen N. S.. 2021. “Human‐Centered Artificial Intelligence in Education: Seeing the Invisible Through the Visible.” Computers and Education: Artificial Intelligence 2: 100008. [Google Scholar]
  60. Yannier, N. , Hudson S. E., and Koedinger K. R.. 2020. “Active Learning Is About More Than Hands‐On: A Mixed‐Reality AI System to Support STEM Education.” International Journal of Artificial Intelligence in Education 30, no. 1: 74–96. 10.1007/s40593-020-00194-3. [DOI] [Google Scholar]
  61. Zawacki‐Richter, O. , Marín V. I., Bond M., and Gouverneur F.. 2019. “Systematic Review of Research on Artificial Intelligence Applications in Higher Education–Where Are the Educators?” International Journal of Educational Technology in Higher Education 16, no. 1: 1–27. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. PRISMA flowchart showing breakdown of identification and screening in the literature search.

CA-38-552-s001.docx (40KB, docx)

Data Availability Statement

The authors have nothing to report.

Articles from Clinical Anatomy (New York, N.y.) are provided here courtesy of Wiley

RESOURCES