Digital Twins allow for designing, operating, and optimizing running systems by replicating the operations of the physical components in a digital environment. Interconnectivity between Digital Twin models and corresponding real-world counterparts facilitates continuous examining of the actual system, resulting in nearly real-time analysis and decision-making support. Validation is an integral part of Digital Twins as underlying models must accurately reflect the corresponding physical systems according to predefined objectives. The near real-time nature of Digital Twins demands a continuous validation process to ensure models’ accuracy. Labor-intensive manufacturing, where humans are at the heart of manufacturing processes, is a sector that encompasses a wide range of industries, from toys and apparel to medical devices and automotive components. This sector continues to play a vital role in emerging economies, offering employment opportunities that mitigate poverty and enhance social stability. Enabling Digital Twins for labor-intensive manufacturing systems opens many opportunities towards humancentricity and improvement of well-being of human operators. In these systems, however, human data must also be considered for Digital Twin development and the corresponding validation processes. Handling human data further complicates the creation and validation of Digital Twins. To the best of our knowledge, there has not been a comprehensive study on the validation of Digital Twins in labor-intensive manufacturing. In this paper, we review Digital Twin validation in manufacturing, focusing on systems that feature data from human operations. As a result, we outline the current challenges of validation of Digital Twins in labor-intensive manufacturing environments and suggest future research directions.