Rapid development in computer technology has led to sophisticated methods of analyzing large datasets with the aim of improving human decision making. Artificial Intelligence and Machine Learning (ML) approaches hold tremendous potential for solving complex real-world problems such as those faced in rehabilitation. These techniques are especially appealing in work disability contexts where large amounts of data are collected, such as workers’ compensation and insurance settings. This lecture will discuss attempts to develop and evaluate clinical decision support tools built using ML for identifying risk factors for delayed return-to-work and selecting interventions for patients with musculoskeletal conditions.