As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of data (e.g., histopathology images, clinical pathology and other study data, adverse effects database, mechanistic literature), variable reporting timelines and heightened regulatory expectations. This white paper examines the emerging role of agentic artificial intelligence (AI) in addressing these issues through coordinated workflow orchestration, data integration, and pathologist-in-the-loop report generation. Based on a closed-door roundtable held during the 2025 Society of Toxicologic Pathology (STP) Annual Meeting and follow-on discussions, this paper synthesizes the perspectives of leading toxicologic pathologists, toxicologists, and AI developers. It outlines the key pain points in current reporting workflows, identifies realistic near-term use cases for agentic AI, and describes major adoption barriers including requirements for transparency, validation, and organizational readiness. A phased adoption roadmap and pilot design considerations are proposed to help support responsible evaluation and deployment of agentic AI system in nonclinical settings. The paper concludes by emphasizing the need for coordinated efforts across pharmaceutical organizations, CROs, academia, and regulators to establish shared standards, benchmarks, and governance frameworks that will lead to safe, transparent, and trustworthy integration of AI into toxicologic science.

翻译：随着非临床毒理学研究的数量和复杂性持续增加，毒理病理学报告撰写面临着持续的挑战，包括数据来源分散（例如组织病理学图像、临床病理学及其他研究数据、不良反应数据库、机制性文献）、报告时间线多变以及监管期望的提高。本白皮书探讨了代理人工智能在通过协调工作流程编排、数据整合以及病理学家参与循环的报告生成来解决这些问题方面的新兴作用。基于2025年毒理病理学会年会期间举行的闭门圆桌会议及后续讨论，本文综合了领先的毒理病理学家、毒理学家和人工智能开发者的观点。文章概述了当前报告工作流程中的关键痛点，确定了代理人工智能在近期切实可行的应用场景，并描述了包括透明度要求、验证需求和组织准备度在内的主要采用障碍。文章提出了分阶段采用路线图和试点设计考虑，以支持在非临床环境中对代理人工智能系统进行负责任的评估和部署。最后，本文强调需要制药企业、合同研究组织、学术界和监管机构协同努力，建立共同的标准、基准和治理框架，以确保人工智能安全、透明、可信地融入毒理科学。