Digital pathology platforms with integrated AI algorithms: a structured landscape analysis and recommendations for clinical implementation

Abstract

Pathology is undergoing a fundamental change as slide-based diagnostics transition toward digital workflows. Advances in whole slide imaging, data management, and computational analysis have created the basis for integrating artificial intelligence (AI) into diagnostic routines. However, the growing number of commercial digital pathology platforms poses challenges for selecting clinically suitable and regulatory-compliant solutions. A structured multi-source approach was conducted to identify commercially available digital pathology platforms. Vendor and sponsor lists from major digital pathology congresses, the Digital Pathology Association vendor catalog, and a structured web search were performed. Predefined eligibility criteria were applied to identify the relevant platforms. Suitable parameters were defined and validated by pathology and IT experts to then compare the included platforms, supplemented by direct vendor communication for selected implementation-related parameters, such as support and pricing modalities. Fourteen platforms met the eligibility criteria. Moderate variation was observed across platforms, particularly regarding deployment models, whole slide imaging (WSI) format compatibility, the integration of external AI algorithms, and reported pricing modalities. Based on the market analysis, six practical recommendations were derived to guide platform selection in routine diagnostic workflows. Pathology institutes are under increasing pressure to digitalize their workflows due to rising work volumes and workforce shortages. However, the market for digital pathology solutions is highly dynamic. To ensure sustainable acquisition decisions, the regulatory framework and the site-specific technical infrastructure must be considered. Furthermore, platforms that support the integration of third-party algorithms and offer broad compatibility with whole-slide image formats help to maintain long-term flexibility with regard to future algorithmic developments.

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