P03-09 Establishment of Readiness Criteria for AI-based tools for risk assessment: READYAI

Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are leading to a new era of data-driven innovation, significantly impacting the field of chemical risk assessment. These advanced computational techniques are increasingly being recognized as powerful tools for various applications in toxicology, including data retrieval (e.g. systematic reviews), predictive modeling (e.g. toxicokinetics, quantitative structure-activity relationships (QSARs), read-across), toxicity prediction, and evidence integration. By leveraging AI/ML/DL-driven approaches, researchers and regulatory bodies are working toward reducing reliance on traditional animal testing while enhancing the accuracy, efficiency, time- and cost-effectiveness of chemical safety evaluations. To effectively and reliably integrate these tools into regulatory frameworks, it is essential that regulatory authorities, policymakers, and other stakeholders gain a deeper understanding of their nature, applicability, validity, and inherent limitations. It is equally crucial for computational scientists and AI developers to understand regulatory requirements and prerequisites. This knowledge enables them to develop methods that meet regulatory standards while effectively communicating both the advantages and limitations of these methods for regulatory applications. To maximize the benefits of AI in chemical safety assessment, a skilled and multidisciplinary task force is needed, fostering collaboration among regulators, toxicologists, computational scientists, and AI developers for responsible utilization of AI-driven methodologies. Within this framework, and as part of the Partnership for the Assessment of Risks from Chemicals (PARC) initiative, we have developed the project “Establishment of Readiness Criteria for AI-based Tools for Risk Assessment: READYAI”. This project aims to address the key challenges associated with implementing AI, ML, and DL in toxicology by developing a comprehensive scoring system for evaluating the development, training, and validation of AI-based tools applied in chemical risk assessment. Additionally, it seeks to ensure the consistent application of this scoring system by developers, enabling a more independent and objective assessment of the regulatory applicability of AI/ML/DL-based tools. Key concerns include ensuring the reliability of AI-generated predictions, mitigating biases in training data, improving algorithm interpretability, explainability, reproducibility & transparency, and addressing legal and ethical implications. By defining readiness criteria and adhering to best practice principles, READYAI seeks to facilitate regulatory acceptance of AI/ML/DL tools and promote the responsible application of state-of-the-art AI methodologies on chemical risk assessment.