Kunze Kyle N
Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
HSS J. 2025 Apr 26:15563316251335334. doi: 10.1177/15563316251335334.
Generative artificial intelligence (AI) comprises a class of AI models that generate synthetic outputs based on learning acquired from a dataset that trained the model. This means that they can create entirely new outputs that resemble real-world data despite not being explicitly instructed to do so during training. Regarding technological capabilities, computing power, and data availability, generative AI has given rise to more advanced and versatile models including diffusion and large language models that hold promise in healthcare. In musculoskeletal healthcare, generative AI applications may involve the enhancement of images, generation of audio and video, automation of clinical documentation and administrative tasks, use of surgical planning aids, augmentation of treatment decisions, and personalization of patient communication. Limitations of the use of generative AI in healthcare include hallucinations, model bias, ethical considerations during clinical use, knowledge gaps, and lack of transparency. This review introduces critical concepts of generative AI, presents clinical applications relevant to musculoskeletal healthcare that are in development, and highlights limitations preventing deployment in clinical settings.
生成式人工智能（AI）由一类AI模型组成，这些模型基于从训练模型的数据集中获取的学习内容生成合成输出。这意味着它们可以创建全新的输出，这些输出类似于真实世界的数据，尽管在训练期间没有被明确指示这样做。在技术能力、计算能力和数据可用性方面，生成式AI催生了更先进、更通用的模型，包括扩散模型和大语言模型，这些模型在医疗保健领域具有广阔前景。在肌肉骨骼疾病的医疗保健中，生成式AI的应用可能包括图像增强、音频和视频生成、临床文档和管理任务的自动化、手术规划辅助工具的使用、治疗决策的强化以及患者沟通的个性化。在医疗保健中使用生成式AI的局限性包括幻觉、模型偏差、临床使用中的伦理考量、知识差距以及缺乏透明度。本综述介绍了生成式AI的关键概念，展示了正在开发的与肌肉骨骼疾病医疗保健相关的临床应用，并强调了阻碍其在临床环境中部署的局限性。
