cs.SEAug 27, 2026

STEP: A Modular Silent Trial Engine for Operational Evaluation of Digital Pathology AI in Routine Workflow

Authors: Gabriele Campanella, Matthew Croken, Olga Lukatskaya, Jane Houldsworth, Ricky Kwan, Peter Schüffler, Chad Vanderbilt

Organizations: Windreich Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York · Hasso Platner Institute at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York · Department of Pathology, Icahn School of Medicine at Mount Sinai, New York · Institute of Pathology, Technical University of Munich, Munich · 

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Abstract

Prospective silent trials provide an important bridge between retrospective validation of artificial intelligence (AI) models and their use in clinical care by evaluating model performance and operational reliability on live clinical data without influencing patient management. In computational pathology, conducting silent trials requires integration across laboratory information systems, digital pathology infrastructure, computational resources, and model inference pipelines, and these workflows are often implemented using application-specific software. We developed the Silent Trial Engine for Pathology (STEP), a reusable software platform for orchestrating prospective silent trials of computational pathology AI models across heterogeneous clinical and computational environments. STEP separates common trial orchestration from institution-specific data access and compute infrastructure through modular adapter interfaces. The platform supports scheduled case discovery, per-slide inference submission, deterministic idempotency, failure recovery, result and ancillary-data ingestion, persistent trial and run state, and audit logging, with compute adapters supporting local execution and high-performance computing environments using LSF and Slurm. STEP was deployed at three institutions to support prospective silent evaluation of EAGLE, an AI model for predicting EGFR mutation status from hematoxylin and eosin-stained whole-slide images. By separating trial-level workflow logic from site-specific integrations, STEP enables a common execution framework to operate across heterogeneous pathology environments while maintaining durable and auditable trial state. This approach may reduce duplicated engineering effort and facilitate systematic real-world evaluation of computational pathology AI before interventional clinical deployment.

Figures & tables

Figure 1: STEP architecture and silent-trial workflow. (a) STEP separates common trial orchestration from site-specific data access and computational infrastructure. The runtime scheduler manages trial registration and scheduled execution, while the orchestrator service coordinates case scanning, inference submission, and result ingestion. Data and compute adapters provide interfaces to local whole-slide image (WSI) repositories, laboratory information systems (LIS), and computational resources. Trial configuration, run state, and results are maintained in the MongoDB repository and exposed through the API layer and read-only dashboard. (b) Operational life cycle of a STEP trial. Following registration, STEP periodically scans for eligible cases, creates and submits inference jobs, executes model inference through the configured compute adapter, ingests completed results, and persists trial state. The scan-to-persistence sequence is repeated according to the configured trial schedule.
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