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Review of state‐of‐the‐art AI tools and methods for screening, extracting and evaluating NAMs literature in the context of chemical risk assessment

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Disclaimer: The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.


The future of risk assessment cannot neglect to consider the vast literature produced through the application of new approach methodologies (NAMs). This, though, constitutes a challenge for the risk assessor, as the production in this context is huge and heterogeneous both for the methods applied and the standardisation and quality of the results. The integration of results 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 the most promising source for help among modern technologies supporting automation of manual tasks. Within the broader context of investigating possible applications of AI to achieve this goal, the present paper illustrates an evaluation framework to quantitatively assess these tools and methods and, in turn, support the selection of the most promising among them for a (at least partial) automation of the overall workflow. It also provides a survey of the state‐of‐the‐art tools and methods identified at present which can then be assessed for specific use cases with the help of the introduced evaluation framework.