Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness

TL;DR

This review considers how emerging AI systems can be responsibly integrated into medical practice by connecting deployable clinical relevance with downstream analytical capabilities and their technical maturity, operational readiness, and economic and regulatory context.

Abstract

Recent breakthroughs in artificial intelligence through foundation models and agents have accelerated the evolution of computational pathology. Demonstrated performance gains reported across academia in benchmarking datasets in predictive tasks such as diagnosis, prognosis, and treatment response have ignited substantial enthusiasm for clinical application. Despite this development momentum, real world adoption has lagged, as implementation faces economic, technical, and administrative challenges. Beyond existing discussions of technical architectures and comparative performance, this review considers how these emerging AI systems can be responsibly integrated into medical practice by connecting deployable clinical relevance with downstream analytical capabilities and their technical maturity, operational readiness, and economic and regulatory context. Drawing on perspectives from an international group, we provide a practical assessment of current capabilities and barriers to adoption in patient care settings.

Figures (4)

• Figure 1: The evolution of multimodal pathology foundation models from passive diagnostic aids to autonomous clinical orchestrators. This framework represents a paradigm shift in computational pathologycampanella2024computational, where the integration of disparate data streamsseoni2024allTrident enables models to transcend traditional morphological assessment and ground abstract visual features in molecular truthkather2024biomarkergulati2025spatialtrans. By leveraging self-supervised alignment and structural modelingchen2022fastUNI, these systems bridge the gap between gigapixel-scale imaging and actionable prognostic insights, uncovering sub-visual determinants of survival and immune evasion that often escape manual evaluationzimmermann2024virchow2. The hierarchical application tiers reflect an increasing complexity of clinical utility, moving from routine phenotypic characterization toward "virtual assays"AIVCVirTues that map the spatial and molecular landscape of the tumor microenvironment without additional tissue consumption. Ultimately, the transition toward agentic reasoning, facilitated by a "Supervisor-Explorer" architecture, transforms the AI from a black-box classifier into a transparent diagnostic partner that mimics expert cognitive workflows, providing an interpretable reasoning chain for complex clinical decision-making and personalized therapeutic planningferber2025developmentghezloo2025pathfinder.
• Figure 2: Translational Opportunities and Barriers for Clinical Implementation of Pathology FMs . We summarize three fundamental gaps that must be bridged to translate basic pathology AI models into real-world clinical application. (1) Economic and infrastructure reality, including indirect deployment costs, large-scale data storage, long-term maintenance of imaging infrastructure, and unresolved questions regarding reimbursement and sustainability. (2) Technical gap and data challenges, arising from hardware heterogeneity, scanner- and site-specific variability, data quality and bias risks, and the intrinsic biological complexity that limits robustness and generalizability. (3) Safety, liability, and human factors, encompassing risks of generative hallucinations, cognitive automation bias, clinician deskilling, and the need for evolving accountability, regulatory, and legal frameworks for clinical adoption.
• Figure 3: Major Challenges and Translational Opportunities for Clinical Implementation of Pathology FMs We outline Six key directions for advancing the clinical translation of pathology AI FMs . (1) Bridge the gap between technical capabilities and clinical priorities to address subtle, context-dependent diagnostic needs. (2) Improve robustness to pre-analytic variability and inter-institutional heterogeneity to enhance generalizability. (3) Harness generative models for applications like virtual staining and automated reporting, while managing risks related to safety, misinformation, and accountability. (4) Develop multimodal systems that integrate histology with transcriptomics, imaging, and clinical metadata for deeper, personalized disease profiling. (5) Build scalable, cost-efficient infrastructure to support model updates and high-resolution image processing. (6) Navigate evolving ethical, legal, and regulatory frameworks to ensure diagnostic reliability and mitigate the risks of error propagation.
• Figure S1: Timeline of digital pathology milestones illustrating key developments from traditional microscopic analysis to pathology foundation models. The 1960's marked the beginning of computerized microscopy image analysis for disease research through automated examinination of cellular structures. The 1980s saw the introduction of whole slide imaging (WSI)STAMP scanners, enabling the conversion of physical slides into digital images and facilitating data storage and analysis. In the 2000s, commercial WSI scanners became widely available, with AI algorithms beginning to automate pathology tasks through digital image analysis. By the 2020s, AI-based medical devices leveraging CNNs 2019Wang_S_TheAmericanJournal_CNNSeg were developed, enabling diverse data type interpretation and multi-task support, further advancing the automation of disease diagnosis and clinical decision-making. The emergence of pathology foundation models represents the current frontier, moving the field toward comprehensive automation and intelligent tissue analysis.