Exploring the use of Artificial Intelligence (AI) for extracting and integrating data obtained through New Approach Methodologies (NAMs) for chemical risk assessment

The future of risk assessment cannot neglect to consider the vast literature produced through the application of new approach methodologies (NAMs). This, however, constitutes a challenge for the risk assessor, as the availability of data in this context is huge and heterogeneous both for the methods applied and the standardisation and quality of the results. The integration of results generated from NAMs is hence only feasible under some degree of automation of the risk assessment workflow, specifically for searching, extracting and integrating such results in “AOP‐like” knowledge networks (AOP – Adverse Outcome Pathway). Artificial intelligence (AI) with its state‐of‐the‐art methods and tools is one of the most promising sources to support automation of manual tasks among modern technologies. The present paper illustrates the results of the exploration of possible applications of AI to achieve this goal. After the introduction of an evaluation framework to quantitatively assess these tools and methods, the results of the implementation of six selected case studies with the support of a selection of such tools in a dedicated workflow are presented. A qualitative survey of the state‐of‐the‐art tools and methods, which also incorporates the experience gathered during the case study implementation, is then presented. Finally, recommendations are formulated which address the main aspects identified through the case study implementation that should, in the opinion of the authors, be pursued by EFSA in the context of its SPIDO NAMs and AI roadmaps. In summary, potentials for AI tool support could be identified throughout the workflow. Although many of the tasks can be supported by (semi‐)automation, experience showed that subject matter experts need to be involved in all workflow steps.