Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training

Abstract

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student's specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI's knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

Keywords: Artificial intelligence; Machine learning; Medical education; Orthopedics.

Fig. 1

Various types of generative AI models, their functionalities, and examples. Generative AI is a subset of deep learning, which is itself a subset of machine learning, includes several model types with unique functions. Generative Adversarial Networks (GANs), like StyleGAN, employ two neural networks-one generating data, the other detecting fakes to create realistic images. Variational Autoencoders (VAEs), such as Beta-VAE and Sketch-RNN, compress and reconstruct data to identify underlying patterns, generating new data that aligns with these patterns. Transformer models, like GPT and Gemini, excel in text generation and natural language processing, producing coherent and creative outputs. Autoregressive models, including GPT-3 (for text) and PixelRNN (for images), generate data sequentially, using prior outputs to guide each step. Diffusion models, like Imagen and DALL-E 2, start with noisy data and iteratively refine it into coherent images, often from text prompts

Fig. 2

Generative AI workflow and its applications in orthopedic education. The process includes data input (text, images, audio, etc.), preprocessing, training of the model, evaluation, and generation of output based on learned patterns. Outputs may include text, images, audio, video, data analysis, problem-solving insights, virtual assistance, and VR/AR simulations, which can give rise to various applications in orthopedic education such as personalized learning plans and materials, case-based learning, clinical images, exam preparation, and simulation-based training

Fig. 3

Challenges and potential solutions associated with integrating generative AI into orthopaedic education. Key challenges include algorithmic bias, inaccuracies, inconsistent outputs, overreliance on AI, academic dishonesty, and privacy concerns. Potential solutions to overcome these challenges involve using diverse datasets and continuous evaluation, regular updates with real time data, adherence to ethical and regulatory guidelines, employing plagiarism detection tools and promoting equitable access to AI resources