Gupta Nikhil, Khatri Kavin, Malik Yogender, Lakhani Amit, Kanwal Abhinav, Aggarwal Sameer, Dahuja Anshul
Department of Pharmacology, All India Institute of Medical Sciences, Bathinda, Punjab, 151001, India.
Department of Orthopedics, Postgraduate Institute of Medical Education and Research (PGIMER) Satellite Centre, Sangrur, Punjab, 148001, India.
BMC Med Educ. 2024 Dec 28;24(1):1544. 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.
生成式人工智能(AI)以其生成包括文本、图像、视频和音频在内的各种形式内容的能力为特征,已经彻底改变了包括医学教育在内的许多领域。生成式人工智能利用机器学习来创建多样化的内容,实现个性化学习,提高资源可及性,并促进交互式案例研究。这篇叙述性综述探讨了生成式人工智能(AI)在骨科教育和培训中的整合,强调了其潜力、当前挑战和未来发展轨迹。对近期文献进行了综述,以评估当前的应用情况,确定潜在的益处,并概述在骨科教育中整合生成式人工智能的局限性。主要研究结果表明,生成式人工智能通过其各种应用,如提供实时解释、根据学生个体特定需求量身定制的适应性学习材料以及沉浸式虚拟模拟,在加强骨科培训方面具有巨大潜力。然而,尽管具有潜力,但将生成式人工智能整合到骨科教育中仍面临重大问题,如准确性、偏差、输出不一致、伦理和监管问题以及对人工监督的迫切需求。尽管ChatGPT等生成式人工智能模型已经展示出令人印象深刻的能力,但它们目前在骨科考试中的表现仍不理想,这凸显了进一步发展以匹配临床推理和知识应用复杂性的必要性。未来的研究应专注于通过持续研究来应对这些挑战,优化用于医学内容的生成式人工智能模型,探索符合伦理的人工智能使用最佳实践、课程整合,并评估这些技术对学习成果的长期影响。通过扩展人工智能的知识库,提高其解释临床图像的能力,并确保可靠、无偏差的输出,生成式人工智能有望彻底改变骨科教育。这项工作旨在提供一个将生成式人工智能纳入骨科课程的框架,为未来的骨科从业者创造一个更有效、更具吸引力和适应性更强的学习环境。
BMC Oral Health. 2025-4-18
Curr Rev Musculoskelet Med. 2025-4-30
Asia Pac J Ophthalmol (Phila). 2024
Nurse Educ Today. 2025-3
Front Artif Intell. 2025-5-21
Front Med (Lausanne). 2025-5-20
JMIR Med Educ. 2025-5-20
Comput Biol Med. 2025-2
Curr Opin Biotechnol. 2024-10
GMS J Med Educ. 2024
J Biomed Inform. 2024-5
EBioMedicine. 2024-4