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

• Published in: BMC medical education 2024-12, Vol.24 (1), p.1544-18, Article 1544
• Main Authors: Gupta, Nikhil, Khatri, Kavin, Malik, Yogender, Lakhani, Amit, Kanwal, Abhinav, Aggarwal, Sameer, Dahuja, Anshul
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Bone surgery; Chatbots; Content creation; Curricula; Curriculum; Data Analysis; Deep learning; Discovery Learning; Education, Medical - methods; Educational aspects; Educational Change; Educational Technology; Generative artificial intelligence; Humans; Influence of Technology; Integrated Curriculum; Language Processing; Large language models; Machine Learning; Medical education; Medical students; Molecular Structure; Orthopedics; Orthopedics - education; Predominantly White Institutions; R&D; Research & development; Review; Surgeons; Surgery; Teachers; Training
• ISSN: 1472-6920; 1472-6920
• DOI: 10.1186/s12909-024-06592-8

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.