Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology

TL;DR

This paper explores how agentic AI can improve toxicologic pathology reporting by addressing data fragmentation, workflow challenges, and regulatory demands through coordinated, transparent, and validated AI systems.

Contribution

It presents a roadmap and use cases for adopting agentic AI in toxicologic pathology, emphasizing responsible deployment and stakeholder collaboration.

Findings

Identified key pain points in current workflows.

Proposed phased adoption and pilot strategies.

Highlighted barriers like transparency and validation.

Abstract

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.