Systematic reviews are essential in toxicology for evaluating chemical hazards and exposure in a transparent and rigorous manner. However, they can quickly become time-consuming and resource-intensive, particularly when handling large volumes of literature. Recent advancements in AI have proven useful for reducing this effort, from streamlining search strategy development to assessing study quality. AI offers significant advancements though selecting the appropriate approach requires expert judgment to ensure the methods align with research objectives. The expert’s role is crucial not only in choosing the right AI model but also in verifying the accuracy of AI-generated results, ensuring that the output is contextually relevant, biologically sound, and scientifically rigorous. This presentation will highlight various case studies that combine AI-techniques and expert insight into toxicological assessment workflows. Case studies utilizing techniques like topic modeling, machine learning-driven literature prioritization, and use of LLMs will demonstrate how these tools are transforming key stages of the review process. By examining the strengths and limitations of AI tools, this session will emphasize the importance of expert oversight in maintaining methodological rigor and ensuring high-quality results at every stage of the review process, while highlighting the need for responsible AI practices in evidence synthesis.