Over the past decade, and especially in recent years, AI has permeated news, politics and many aspects of everyday life (eg, chatbots, virtual assistants, social media, smart devices). Biomedical and public health researchers and practitioners are also finding uses for AI. AI algorithms have been used to radiography and biomedical imagery, medical records1 and to identify built environment features associated with health outcomes. What potential do they have for injury prevention and control? A brief literature search suggests these methods are also being adopted by the field: examine road infrastructure safety and crashes,2 predict the severity of motorcyclist injuries,3 detect motorcycle helmet use,4 predict and prevent sport injuries,5 and to identify built environment typologies related to firearm violence.6 What implications do these have for the field and how can we adopt them along with more traditional approaches?

AI refers to both a set of algorithmic approaches to analysing data, as well as the theoretical underpinnings of the discipline that has the goal of creating or simulating intelligence.7 AI encompasses both machine learning and deep learning, the latter becoming most synonymous with AI in recent years. Deep learning algorithms underlie natural language processing (NLP) models, speech recognition used in voice assistants, object detection and recognition in video and images like facial recognition and self-driving vehicles, and generative AI that creates different media. AI as a field has existed since the 1940s and 1950s and many of the early successful efforts centred around text and language, though other areas of research included computer vision, speech and audio, and video.7 8 The field has passed through cyclical ups and downs, with current interest and progress in AI beginning in the early 2010s when newer models demonstrated high accuracy at or near human-level performance.7 This …