代理人工智能在毒理病理学中的潜在作用
Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology
AI总结：
本文探讨了代理人工智能在毒理病理学报告中的应用，分析了当前工作流程中的痛点，提出了分阶段的采用路线图，并强调了跨领域协作的重要性。
摘要：
随着非临床毒理学研究的体积和复杂性持续增加，毒理病理学报告面临着持续的挑战，包括数据来源碎片化（例如，组织病理学图像、临床病理和其他研究数据、不良反应数据库、机理文献），报告时间线的不一致以及监管要求的提高。本文白皮书探讨了代理人工智能（AI）在通过协调的工作流程编排、数据整合和病理学家在环报告生成来解决这些问题中的新兴作用。基于2025年毒理病理学学会（STP）年度会议期间举行的闭门研讨会及后续讨论，本文综合了毒理病理学家、毒理学家和AI开发人员的观点。它概述了当前报告工作流程中的关键痛点，识别了代理AI在短期内的可行用例，并描述了主要的采用障碍，包括对透明性、验证和组织准备性的要求。提出了一种分阶段的采用路线图和试点设计考虑因素，以帮助支持代理AI系统在非临床环境中的负责任评估和部署。本文最后强调了需要制药公司、CROs、学术界和监管机构之间的协调努力，以建立共享的标准、基准和治理框架，从而实现AI安全、透明和可信地融入毒理学科学。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
