Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children's natural environments by non-specialists. The purpose of this systematic review is to identify and characterise potentially scalable digital tools for direct assessment of autism spectrum disorder risk in early childhood. In total, 51,953 titles, 6884 abstracts and 567 full-text articles from four databases were screened using predefined criteria. Of these, 38 met inclusion criteria. Tasks are presented on both portable and non-portable technologies, typically by researchers in laboratory or clinic settings. Gamified tasks, virtual-reality platforms and automated analysis of video or audio recordings of children's behaviours and speech are used to assess autism spectrum disorder risk. Tasks tapping social communication/interaction and motor domains most reliably discriminate between autism spectrum disorder and typically developing groups. Digital tools employing objective data collection and analysis methods hold immense potential for early identification of autism spectrum disorder risk. Next steps should be to further validate these tools, evaluate their generalisability outside laboratory or clinic settings, and standardise derived measures across tasks. Furthermore, stakeholders from underserved communities should be involved in the research and development process.