Forensic digital histopathology datasets as infrastructure for trustworthy artificial intelligence deployment

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Abstract
Digital pathology has transformed anatomic pathology by enabling whole-slide imaging, remote review, reproducible annotation, image analysis, and the development of artificial intelligence-based decision-support tools. In forensic pathology, however, the transition from glass slides to digital forensic histopathology remains in its early stages, despite recent evidence supporting the feasibility and diagnostic reliability of whole-slide imaging in medico-legal practice. The subsequent crucial step involves not merely digitizing forensic slides but also developing curated, multicentric, legally governed, and controlled-access datasets comprising forensic histopathological images. Such datasets are essential for education, interobserver harmonization, external quality assessment, and the development of reproducible quantitative tools suitable for forensic applications. This perspective paper argues that forensic pathology should regard datasets as scientific infrastructure rather than as passive image collections. An artificial intelligence-ready forensic histopathology dataset should include whole-slide images, standardized annotations, technical acquisition parameters, histological processing information, forensic metadata, quality-control records, reference standards, uncertainty labels, and explicit governance rules. Although important lessons can be drawn from anatomic pathology datasets, forensic pathology presents specific challenges, including low case numbers, post-mortem artifacts, heterogeneous sampling, variable fixation and preservation conditions, judicial and privacy concerns, and differences in national regulation of post-mortem data. We therefore propose a literature-informed conceptual framework for the development of forensic histopathology datasets, intended for forensic pathologists, researchers, institutional stakeholders, and AI tools developers. The framework is intended to support the development of shared resources for education, research, and AI development and validation, thereby providing a foundation for trustworthy and scientifically defensible computational applications in forensic pathology.

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