The application of big data and artificial intelligence (AI) in Thoroughbred racing has the potential to transform equine health management and injury prevention. Modern racing jurisdictions collect vast datasets, including race results, training records, and in some cases, biometrics and linked veterinary information. However, the full potential of these data resources often remains underutilized. Analysing large-scale data, such as these, can enable predictive analytics that move the industry from a reactive to a more proactive approach to horse welfare.
AI techniques, particularly machine learning, can help to identify subtle patterns in data that precede adverse events such as catastrophic musculoskeletal injury or sudden cardiac death. These models may also help identify horses most likely to benefit from advanced diagnostic imaging or other targeted interventions, optimizing both welfare outcomes and resource allocation.
This presentation will consider the importance of transparent, explainable AI (and other big-data) models to build trust among trainers, veterinarians, and regulators. The potential for real-time predictive systems to support decision-making on training modifications, race entry, and pre-race screening will also be discussed – with a particular focus on the need for data with as wide a scope as possible. Finally, the imperative that all models (AI or not) are validated for efficacy and predictive ability, before being used for decision making, will be emphasised.
The future of risk prediction in Thoroughbred racing lies in leveraging big data and AI, not as a replacement for expert clinical judgment but, as a tool to enhance it. By embracing these technologies, the industry has an opportunity to further minimise the risk of preventable injuries, improve horse welfare, and help to sustain public confidence in the sport at a time when the social licence to operate is under ever greater scrutiny.