【Abstract】 In recent years, artificial intelligence (AI) technology has rapidly advanced in the field of medical imaging, highlighting some issues in traditional teaching models for medical imaging techniques. This paper analyzes the applications of AI in assisting image interpretation, generating teaching content, and building immersive learning environments. AI not only effectively enhances teaching efficiency and enables personalized learning but also promotes a transformation in the role of teachers. However, AI faces challenges such as insufficient model explainability, potential algorithmic bias, and data security concerns. It is recommended to strengthen human-computer collaboration, promote the integration of multi-modal technologies, and improve relevant ethical norms to facilitate deeper and higher-quality applications of AI in medical imaging education. 【Keywords】 Artificial Intelligence; Medical Imaging; Teaching Reform; Generative AI; Virtual Reality
Medical imaging technology is a crucial pillar of modern clinical medicine, providing key evidence for disease diagnosis, treatment, and prognosis assessment. With the rapid development of imaging techniques such as CT, MRI, and PET, the complexity and dimensionality of medical imaging data have increased dramatically [1]. This not only raises higher requirements for clinicians' image interpretation skills but also puts significant pressure on traditional medical imaging education. Traditional teaching methods, such as classroom lectures and limited clinical internships, are increasingly inadequate for helping students handle large volumes of high-dimensional imaging data and make accurate diagnoses quickly. Therefore, how to innovate teaching methods to improve teaching quality and efficiency has become an urgent issue in the field of medical imaging technology education.
In recent years, particularly deep learning (DL)-based machine learning technologies, have brought about profound changes in the field of medical imaging [2]. AI algorithms have demonstrated potential that approaches or even surpasses human experts in areas such as image reconstruction, lesion segmentation, disease diagnosis, and prognosis prediction [3,4]. For instance, AI technology is now widely used in the imaging analysis of diseases like lung cancer, breast cancer, and prostate cancer, significantly enhancing diagnostic accuracy and efficiency [5,6,7]. The rapid development of AI has not only transformed clinical practices but also provided new possibilities for innovation in medical imaging education. Integrating AI into the teaching process can help address some of the challenges in traditional education. For example, AI-assisted diagnostic systems can provide real-time feedback to students, virtual reality (VR)/augmented reality (AR) technologies can create immersive learning scenarios, and generative AI can generate large amounts of synthetic training data to supplement real cases [8]. However, the application of AI in teaching also faces multiple challenges, including technical, ethical, and data security issues [9]. Therefore, systematically reviewing the current status, advantages, and challenges of AI in medical imaging education, and envisioning future trends, is of significant theoretical and practical value for promoting the modernization of medical education and cultivating versatile talents adapted to the needs of smart healthcare.
- Main Applications of Artificial Intelligence in Medical Imaging Education As AI technology continues to be deeply integrated into clinical practice, it is playing an increasingly important role in medical imaging education, fundamentally changing the way knowledge is imparted and skills are trained. These innovative applications cover various aspects, including assisting image interpretation, generating teaching content, and building immersive learning environments, providing learners with more efficient and interactive learning experiences.
One of the core applications of AI in medical imaging is image recognition and diagnostic assistance. This capability is gradually becoming a powerful teaching tool. Deep learning models based on convolutional neural networks (CNNs) can automatically learn from vast amounts of imaging data, extracting subtle features that are difficult for the human eye to detect, which are then used for automatic detection, segmentation, classification, and diagnosis of lesions [5,10]. For example, in scenarios such as lung nodule detection, breast cancer screening, and early detection of pancreatic cancer, AI-assisted diagnostic systems have shown high sensitivity and specificity [6,11,12]. In teaching settings, these AI systems can act as " tireless sparring partners" and "standardized evaluators." Students can practice reading images under the guidance of AI, comparing their diagnostic results with AI analyses to receive immediate and objective feedback. Research shows that in prostate cancer MRI diagnosis, AI systems perform even better than radiologists in detecting clinically significant prostate cancer [13]. Such systems can serve as a reference standard for trainees to self-assess and improve their skills. Additionally, AI-assisted systems can highlight suspicious lesion areas and use explainability techniques like saliency maps to show the basis for their judgments, helping students understand the relationship between key imaging features and diseases, thus accelerating the learning process [14,15]. For instance, in thyroid nodule ultrasound diagnosis, AI-assisted strategies have significantly improved the diagnostic level of radiologists [16]. Similarly, AI-assisted colonoscopy not only increases polyp detection rates but also helps less experienced doctors improve their diagnostic skills, demonstrating its great potential in teaching and training [17].
Beyond diagnostic assistance, AI also shows significant potential in innovating teaching content. Traditional medical imaging education often faces limitations due to the scarcity and difficulty in obtaining real clinical cases, and data privacy and ethical issues further challenge the expansion of teaching resources. Generative AI, particularly generative adversarial networks (GANs) and diffusion models, offer a new solution to this problem. These technologies can generate highly realistic synthetic medical images, such as CT, MRI, and PET images [8,18]. More impressively, through simple text commands, models can generate imaging data for specific diseases and imaging modalities, greatly enriching the case library [19]. These synthetic data not only help train more robust AI models but, more importantly, provide students with a large amount of diverse and privacy-free practice material. Students can repeatedly practice in simulated environments, encountering various variants of rare or typical cases, which is difficult to achieve in traditional teaching. On the other hand, the rapid development of large language models (LLMs) has also driven the automated generation of teaching content and the construction of personalized learning paths. LLMs possess strong natural language understanding and generation capabilities, extracting knowledge from massive literature, textbooks, and clinical reports to automatically generate structured teaching materials, exercises, and summaries [20]. For example, research has used LLMs to automatically generate the "impression" section of reports based on imaging findings, with excellent performance in both professionalism and language fluency [21]. In the teaching process, such tools can act as "AI teaching assistants" or "pathology co-pilots," engaging in interactive Q&A sessions with students, explaining complex pathophysiological processes, and recommending personalized learning materials and exercises based on students' knowledge gaps. Studies have shown that in surgical skill training, AI mentor systems perform even better than remote expert guidance [23], demonstrating the significant potential of AI in providing personalized and adaptive feedback.
While expanding two-dimensional teaching resources, the combination of AI and immersive technologies is opening up a new three-dimensional world for medical imaging education. Virtual reality (VR) and augmented reality (AR) technologies, by creating immersive or information-enhanced environments, bring new possibilities to medical imaging education. VR can create entirely virtual operating rooms or anatomy laboratories, while AR can overlay digital information—such as 3D organ models and lesion locations—onto real-world scenes. When combined with AI, the educational value of these technologies is significantly enhanced. AI algorithms can automatically perform high-precision 3D reconstructions from 2D images like CT and MRI, generating virtual organ models with patient-specific characteristics [24]. Students can rotate, scale, and virtually dissect these models in a VR environment, intuitively understanding complex spatial anatomical relationships and their correspondence to lesions. This is particularly important for teaching cardiovascular diseases and complex pancreatic surgeries [25,26]. For example, the VR-Caps project has created a virtual environment for capsule endoscopy, which can be used for developing and testing AI algorithms for disease detection and navigation, as well as serving as an efficient training platform [27]. In interventional radiology and surgical education, AR also shows broad prospects. AI can analyze intraoperative images in real-time and project segmented blood vessels, tumors, and other critical structures onto the patient's body surface or the surgical field, providing "X-ray vision" for operators [1]. Trainees can perform simulations of procedures such as punctures and ablations under AR guidance, gradually mastering complex surgical techniques in a safe and controlled environment. Additionally, AI-driven simulation systems can record each step of the operator's actions and compare them with expert operations, providing precise feedback and skill assessments [23]. This AI, VR/AR-integrated simulation training method not only reduces dependence on physical models and animal experiments but also significantly enhances the realism, interactivity, and effectiveness of training.
- Advantages and Challenges of Artificial Intelligence in Medical Imaging Education Integrating artificial intelligence into medical imaging education has indeed brought revolutionary changes to traditional teaching models, showcasing significant potential in improving teaching efficiency, optimizing teaching quality, and reducing the burden on teachers. However, this process is not without challenges and requires careful attention to technological maturity, ethical norms, and data security.
The core advantage of AI lies in its powerful data processing and pattern recognition capabilities, which can directly translate into improvements in teaching efficiency and quality. First, AI can provide students with immediate and personalized feedback. In traditional teaching, the grading of student assignments and skill assessments often involve delays, and evaluation standards are difficult to standardize. AI teaching systems can analyze students' image interpretations or simulation operations in real-time, providing objective and quantitative evaluations based on predefined expert standards or data-driven models [23]. For example, studies have shown that even nurses with no prior ultrasound experience can successfully capture cardiac ultrasound images of diagnostic quality under the real-time guidance of AI algorithms [28]. This demonstrates that AI can effectively shorten the learning cycle for beginners. Second, AI greatly enriches teaching resources. Through generative AI technology, we can create a large number of diverse virtual cases, covering different variants of rare and common diseases, addressing the contradiction between the lack of high-quality cases and patient privacy protection in clinical teaching [8,19]. Students can practice unlimitedly on these virtual data, accumulating rich "real-world" experience before entering clinical internships. Additionally, AI-driven VR/AR teaching systems can transform 2D imaging data into interactive 3D models, helping students better understand complex anatomical structures and pathological changes, significantly enhancing the immersion and effectiveness of learning [25]. Moreover, AI-assisted diagnostic tools can help students identify subtle lesions or imaging features that are difficult for the human eye to detect, expanding their cognitive boundaries and cultivating their ability to solve clinical problems using advanced tools [14].
While AI improves student learning efficiency, it also creates more value for teachers. In traditional medical imaging education, teachers often need to invest a lot of time in lesson preparation, grading assignments, organizing internships, and individual tutoring, leaving little time for deeper teaching design. With the help of AI technology, teachers can delegate some standardized tasks to the system. For example, AI can automatically grade objective questions and even assist in evaluating student-written imaging reports [21,29]. Teaching platforms can record and analyze each student's learning trajectory, knowledge mastery, and common errors, generating personalized learning reports to help teachers focus on key teaching points and provide targeted guidance [30]. When AI takes on basic teaching tasks, teachers can devote more energy to heuristic teaching, complex case discussions, and the cultivation of clinical thinking. The role of teachers shifts from mere knowledge transmission to guiding students in exploration, stimulating critical thinking, and fostering a spirit of medical humanities [9]. This transformation not only reduces the administrative burden on teachers but also promotes a shift in medical education from teacher-centered to student-centered modern teaching models.
Despite the broad prospects of AI in medical imaging education, its actual promotion still faces many practical challenges. Technically, the robustness and generalization capabilities of models are the primary challenges. Many AI models perform well on training data but often show significant performance drops when faced with new data from different devices or populations [7]. This requires AI tools used in teaching to undergo rigorous multi-center, multi-scenario validation to ensure their stability and reliability. Another key issue is the "black box" nature of models. Most deep learning models lack transparency in their decision-making processes, making it difficult to explain the basis for their judgments. This is particularly challenging in teaching scenarios, where it is essential not only to tell students "what" but also to explain "why." Developing explainable AI technology is crucial for teaching applications [14,15]. Additionally, the development and maintenance of AI models require substantial computational resources and specialized talent, posing high technical and economic barriers for many educational institutions. Reducing usage costs and making AI technology accessible to more schools is a practical issue that needs to be addressed.
Ethically, the application of AI also raises many thought-provoking questions. One of the most prominent issues is algorithmic bias. If the datasets used to train AI are biased in terms of race, gender, socioeconomic background, etc., the model may perpetuate or even amplify these biases, leading to unfair diagnoses for certain groups and inadvertently passing on incorrect medical concepts to students [31]. Another concern is that over-reliance on AI could weaken students' independent judgment and adaptability in handling unexpected situations. It is essential to clarify AI's auxiliary role in teaching, always emphasizing the ultimate decision-making authority and responsibility of human doctors [32]. Furthermore, defining liability is a practical challenge. When AI-assisted teaching leads to errors that cause students to form incorrect understandings, who should bear the responsibility—the developers, educational institutions, or teachers? These issues need to be clearly addressed through the establishment of clear ethical guidelines and operational procedures [9].
Data security is a critical issue that cannot be overlooked in AI teaching applications. Medical imaging data involves a large amount of sensitive personal health information, and protecting this privacy must be a top priority. In the teaching process, whether using real patient data to train models or recording student operation data, strict adherence to data protection regulations such as HIPAA is necessary. Data breaches or improper use can lead to serious legal and ethical risks [33]. To address this challenge, researchers are actively exploring privacy-preserving computing technologies. Federated learning and swarm learning, among other novel algorithms, allow collaborative model training without sharing raw data, effectively balancing data utilization and privacy protection [34,35]. Using high-quality synthetic data to replace real patient information is also a promising solution [36]. These technologies provide important guarantees for safe and compliant AI teaching.
- Future Trends in Medical Imaging Education Driven by Artificial Intelligence With the continuous development of AI technology, especially breakthroughs in foundational models and multi-modal learning, the application of AI in medical imaging education is evolving towards smarter, more integrated, and more collaborative directions. Future teaching models will not simply be a combination of various technologies but will be a new teaching ecosystem deeply integrated with AI.
Future medical imaging education will increasingly rely on comprehensive intelligent teaching platforms. These platforms will no longer be a combination of single-function tools but will provide support for the entire process, from theoretical learning and skill training to assessment and evaluation, as a "one-stop" solution. At the core will be general medical AI models [37]. Unlike current "narrow" AI models designed for specific tasks, GMAI foundational models, through self-supervised learning on vast and diverse medical data, can flexibly handle multiple input modalities such as imaging images, text, and genomics data, and perform a variety of tasks, including answering open-ended questions, generating teaching content, and explaining diagnostic logic. For example, future intelligent teaching platforms might integrate visual-language models similar to "PathChat" or "Med-PaLM" [20, 22]. Students can upload an imaging picture and ask the platform, "What is the most likely condition for this high-signal area?" The platform can answer using natural language combined with image annotations. The platform can also dynamically adjust the learning path based on the student's performance, pushing personalized learning resources and virtual cases. This highly integrated, personalized, and interactive platform will make the learning process more efficient and engaging.
The development direction of these intelligent teaching platforms will inevitably involve the deep integration of multi-modal data to meet the urgent need for interdisciplinary collaboration in modern medicine. Current medical practice increasingly emphasizes multidisciplinary team collaboration and precision medicine, where diagnostic and treatment decisions often require integrating information from various sources such as imaging, pathology, genomics, and clinical records [38]. AI shows great potential in fusing multi-modal data. For example, research has successfully developed "radiopathomics" models that integrate MRI images and biopsy pathology slices to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer, outperforming single-modality models [39,40]. This trend places new demands on medical imaging education, requiring the cultivation of students' ability to integrate information across disciplines. Future teaching will introduce more interdisciplinary content, such as cases that include complete patient information, including CT images, pathology reports, biochemical test results, and electronic medical records. AI systems can demonstrate how to extract key features from these multi-modal data and build predictive models to assess patient outcomes or treatment responses [41,42]. Students will need to learn to understand the working principles of these AI models and try to use multi-dimensional information for comprehensive clinical decision-making, thereby becoming truly versatile medical professionals adapted to the era of precision medicine.
As AI takes on more standardized and repetitive tasks in teaching, the role of human teachers will undergo a profound transformation, forming a new type of "human-machine collaboration" teaching model [43]. AI will not replace teachers but will serve as a capable assistant, working together to achieve teaching goals. In the future, the core responsibilities of teachers will gradually shift from knowledge transmission to higher-level guidance, inspiration, and supervision. Teachers will become designers and evaluators of AI-integrated courses, systematically planning the teaching process that integrates AI tools based on teaching objectives and continuously tracking course effectiveness to promptly identify and correct any biases or limitations introduced by AI [44]. At the same time, teachers will place greater emphasis on cultivating students' critical thinking, guiding them to rationally examine AI outputs, understand the logical boundaries, potential biases, and applicable scenarios behind AI, and learn to question and verify AI-generated content [9]. More importantly, teachers will continue to play the role of demonstrators in complex clinical decision-making and humanistic care. While AI excels at data processing and logical reasoning, it still falls short in handling complex and dynamic clinical situations and conducting effective doctor-patient communication. Through case discussions and bedside teaching, teachers can demonstrate how to make comprehensive judgments in real medical environments and how to integrate technical tools with humanistic care, thereby cultivating students' overall clinical competence and communication skills.
To ensure the stable development of AI technology in education, future work should focus on the following key directions: First, strengthening high-quality educational application research. There is an urgent need for more high-level evidence-based research, such as randomized controlled trials (RCTs), to scientifically evaluate the long-term effects of different AI teaching tools in real educational settings. Second, constructing and sharing high-quality teaching datasets. Encouraging multi-center collaborations and actively developing synthetic data generation techniques and federated learning methods to safely and compliantly expand teaching resources. Third, developing and promoting AI tools with explainability and fairness. Future research should emphasize explainable AI (XAI) to enable students to understand the underlying principles, not just the results, and establish strict algorithm auditing mechanisms to correct biases. Fourth, updating educational philosophies and curricula. Educational institutions should proactively integrate AI literacy into medical curricula, cultivating students' abilities to understand, apply, and evaluate AI tools, and supporting teachers in transitioning their roles.
- Conclusion and Outlook The rapid development of artificial intelligence is reshaping the teaching model of medical imaging technology. From image recognition-assisted teaching to content auto-generation, from personalized learning paths to VR/AR simulation training, AI demonstrates multifaceted application potential. It provides immediate feedback, standardized assessments, and a large number of virtual cases, effectively enhancing teaching efficiency and quality. At the same time, we must recognize that the application of AI in teaching still faces numerous challenges, including technical bottlenecks such as model robustness, generalization capabilities, and explainability, as well as complex ethical and legal issues such as algorithmic bias, liability definition, and data privacy and security. In response to these challenges, we need to maintain a cautious and critical attitude, establish a comprehensive regulatory framework and ethical guidelines, and ensure the safe, fair, and responsible application of AI technology in education.
Looking ahead, medical imaging education will evolve towards smarter, more integrated, and more collaborative directions. Intelligent teaching platforms centered around general medical AI are expected to provide comprehensive, personalized, and interactive support throughout the learning process. The application of multi-modal data fusion technology will break down disciplinary barriers and cultivate students' ability to solve complex clinical problems across domains. In this process, AI will form a new collaborative relationship with teachers—AI will efficiently transmit knowledge and skills, while teachers will focus on guiding thoughts and nurturing humanistic values.
In summary, artificial intelligence offers significant opportunities for the modern transformation of medical imaging education. Through collaborative efforts from academia, industry, and education, carefully addressing challenges, and actively promoting innovation, we can look forward to a new era of deep AI empowerment and human-machine collaboration, laying a solid foundation for cultivating the next generation of outstanding medical professionals.
