Introduction: Pharmaceutical clinical development demands pathology workflows that are reproducible, scalable, and capable of integrating AI-derived insights across heterogeneous imaging environments. Existing approaches typically rely on siloed tools that fragment data management, model execution, and result interpretation - creating bottlenecks that impede both study efficiency and scientific rigor. The emergence of foundation models, generative AI (GenAI), and end-to-end digital pathology platforms presents an opportunity to fundamentally transform these workflows through automation-driven efficiency gains and unified AI orchestration.

Methods: PathoSuite is an Merck internally developed digital pathology platform unifying the full analytical lifecycle. PathoVault handles slide ingestion, access control, and data harmonization; PathoFusion orchestrates AI model execution across oncology, immunology, and metabolic disease indications; PathoMetrics converts outputs into quantitative measures and embedding-based representations for biomarker discovery; and PathoMark UI provides role-appropriate access for visualization, quality control, and AI-assisted analysis. GenAI-powered coding assistants reduced engineering effort by ~50% and accelerated delivery timelines by 2x.

Results: Deployed within Merck's clinical and translational pathology infrastructure, PathoSuite consolidates previously fragmented workflows into a single platform. Standardized data ingestion across vendors, structured AI inference pipelines, and interpretable feature outputs have collectively improved analytical consistency, reduced manual overhead, and accelerated project timelines by 3x.

Conclusion: PathoSuite establishes a enterprise-grade foundation for AI-enabled digital pathology in pharmaceutical development. It supporting reproducible, high-throughput imaging workflows across therapeutic areas and study types.

Learning Objectives:

1. Describe how a modular platform unify data ingestion, AI inference, and result interpretation across pathology environments.
2. Explain how foundation model-driven pipelines enable scalable quantitative analysis across disease indications.
3. Recognize how interpretable feature outputs can broaden AI adoption among stakeholders in a pharmaceutical environment