Generative Versus Nongenerative Artificial Intelligence

Abstract

Artificial intelligence (AI) is a colossal buzzword, a confusing subject matter, but also an inevitable reality. Generative and nongenerative AI are the 2 core subtypes of AI. Generative AI uses current data to understand patterns and generate new information, and it is especially valuable in producing synthetic medical images, enhancing surgical simulations, and expanding training datasets. Techniques such as generative adversarial networks (GANs), large language models (LLMs), and variational autoencoders (VAEs) allow for the creation of realistic simulations, text, and models that can be used for perioperative communication and planning. Conversely, nongenerative AI is centered on the examination and categorization of pre-existing data to formulate predictions or decisions—the most popular denomination namely machine learning. This approach is instrumental in tasks such as forecasting surgical outcomes, segmenting medical images, and determining patient risk profiles. Models such as convolutional neural networks (CNNs), random forests, and support vector machines (SVMs) are widely used for these purposes, demonstrating high accuracy and reliability in clinical decision making. Although generative AI offers innovative tools for creating new data and simulations, nongenerative AI excels in analyzing existing data to inform patient care. Both approaches have the potential of supporting clinical workflows to automate redundancies and improve efficiencies. However, there are also limitations in the application of AI in orthopaedics, including the potential for bias in models, the challenge of interpreting AI-driven insights, and the ethics of oversight. As the integration of AI in orthopaedics continues to grow, it is essential for practitioners to understand these technologies' capabilities and limitations to harness their full potential and establish appropriate governance.