保护
背景(考古学)
计算机科学
风险分析(工程)
质量(理念)
人工智能
数据科学
业务
生物
医学
认识论
古生物学
护理部
哲学
作者
Maria Vittoria Togo,Fabrizio Mastrolorito,Alessia Orfino,Elisabetta Anna Graps,Anna Rita Tondo,Cosimo Altomare,Fulvio Ciriaco,Daniela Trisciuzzi,Orazio Nicolotti,Nicola Amoroso
标识
DOI:10.1080/17425255.2023.2298827
摘要
The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.
科研通智能强力驱动
Strongly Powered by AbleSci AI