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Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

Mohsin Bilal, Aadam, Manahil Raza, Youssef Altherwy, Anas Alsuhaibani, Abdulrahman Abduljabbar, Fahdah Almarshad, Paul Golding, Nasir Rajpoot

arXiv:2502.08333v1cs.CV

TL;DR

Computational pathology is moving from specialized models toward large, adaptable, multimodal and generative foundation models, raising questions about their clinical integration and evaluation. This review synthesizes their definitions, applications, development challenges, and benchmarking needs, finding substantial capability alongside persistent limitations in generalization, safety, and adoption. It concludes that stronger multimodal training, efficient and interpretable models, rigorous clinical validation, and standardized benchmarks are needed for reliable real-world use.

  • Problem

    Clinical adoption is limited by poor generalization on challenging zero-shot tasks, insufficient model-level evaluation, and unresolved safety, reliability, and ethical concerns.

  • Method

    The paper reviews pathology foundation models, their definitions, applications, training and adaptation approaches, evaluation challenges, and clinical deployment requirements.

  • Results

    3.1 million WSIs and 1.85 billion trainable parameters characterize Virchow2 and Virchow2G, while pathology models demonstrate expanding multimodal, generative, and multipurpose capabilities.

  • Takeaways & Limitations

    Reliable clinical use requires multimodal learning, efficient and interpretable models, rigorous clinical validation, and standardized benchmarks for generalization and robustness.

  • Takeaways & Limitations

    Current visual-language models perform inferiorly on challenging zero-shot problems relative to supervised counterparts, while data and deployment biases remain concerns.

Abstract

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From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support.

1. Introduction

Foundation models could reshape computational pathology by combining broad cross-domain capabilities with task adaptation, but their clinical value remains uncertain. The review examines persistent barriers including limited data diversity, opaque decisions, weak cross-population generalization, and clinical deployment challenges.

  • Foundation models promise to unify knowledge across histopathology and oncology while adapting to specialized pathology tasks.
  • Reliable development requires careful data curation, model training, and validation because biased datasets may cause models to miss diagnostic subtleties.
  • These models can combine gigapixel histopathology, oncology, and genomics to support complex-disease diagnosis and treatment-response prediction.
  • The review asks whether emerging generative and task-agnostic models offer genuine diagnostic progress compared with specialized counterparts.
  • Existing surveys cover technologies, datasets, and training methods, but differ in their treatment of risks, clinical challenges, opportunities, and translation barriers.
  • Clinical adoption is constrained by limited data diversity, opaque model decisions, poor population-level generalization, workflow integration, and digitization costs.

2. Foundation Models: Definition, Importance, and Technical Foundations

Foundation models mark a shift from task-specific systems toward broadly adaptable models trained at unprecedented scale. In pathology, this evolution has expanded modality, task coverage, and dataset size while intensifying concerns about bias, safety, reliability, and ethical deployment.

  • Machine learning evolved from handcrafted features, through deep representation learning, to foundation models reused across diverse downstream applications.
  • Foundation models are characterized by scale, self-supervision, and adaptability, often spanning diverse datasets and modalities.
  • Pathology models now span image-only, multi-stain, cross-modal, and generative multimodal paradigms.
  • Broad task adaptability creates opportunities but also requires safeguards for robustness, bias, transparency, safety, and ethical deployment.
  • Virchow performs tumor detection, grading, and molecular biomarker prediction, while UNI13 covers over 100 cancer types and nuclei segmentation across 20 tissue types.
  • 3.1 million WSIs and 1.85 billion trainable parameters characterize Virchow2 and Virchow2G, while OmniScreen predicts 1,228 genomic biomarkers across 70 cancers.
  • RudolfV uses 134,000 slides and 1.2 billion patches to support nearly 50 tasks, including tumor-microenvironment classification and segmentation.
  • Training data remain imbalanced across cancers, regions, demographics, and laboratory settings, limiting generalizability across healthcare populations.

3.1. Modeling

Foundation-model architectures are designed to represent complex pathology patterns, scale with data and compute, process multiple modalities, and transfer across tasks. Their modeling requirements also include systematic generalization and adaptation to unseen disease patterns.

  • Foundation models require expressivity, scalability, multimodality, knowledge access, transfer learning, and systematic generalization.
  • Transfer learning supports rapid adaptation with limited data and zero- or few-shot task adaptation through prompting.
  • Systematic generalization combines learned primitives into novel disease-pattern compositions and supports out-of-distribution recognition.
  • Multi-head self-attention captures long-range dependencies across tissue regions and magnification levels.
  • Deep residual and hybrid architectures learn hierarchical features from cellular to architectural patterns.
  • Large parameter counts and task-agnostic pre-training objectives support sophisticated diagnostic capabilities across pathology applications.

3.2. Training

Training foundation models relies on self-supervised learning over large unlabeled multimodal datasets, with design choices balancing information retention, efficiency, flexibility, and computational cost. Generative and discriminative objectives provide different trade-offs for pathology applications.

  • Self-supervised learning mines vast unlabeled multimodal datasets to produce task-agnostic representations without manual annotations.
  • Training must balance domain completeness with computational efficiency so models generalize across tasks and domains.
  • Raw pixels preserve detailed information but slow learning and increase computation, whereas patch embeddings and tokenization improve efficiency while risking information loss.
  • Generative models offer flexible, interactive outputs but are computationally intensive, whereas discriminative models learn faster and more efficiently for high-dimensional data.

3.3. Training Pathology Foundation Models

Pathology foundation models use large-scale self-supervised, multimodal, and architectural strategies to learn representations from images and associated data. Training innovations target robustness, multiscale understanding, efficient deployment, and broader downstream utility.

  • Training Pathology Foundation Models: 40 state-of-the-art models use diverse embedding strategies to compress essential features from whole-slide images and associated textual data.The review compares architectural designs, training details, and data scales across these models.
  • Self-Supervised Learning and DINO-based Models: Self-supervised DINO-based methods use student-teacher distillation between image views, with Virchow2G scaling to a ViT-G architecture containing 1.9B parameters.Extended-context translation is used to preserve cellular morphology during image processing.
  • Self-Supervised Learning and DINO-based Models: Masked image modeling, self-distillation, contrastive learning, and augmentation are combined across models to improve robustness to staining variability, artifacts, and pathology-image diversity.UNI combines self-distillation with masked image modeling, while BROW adds patch shuffling and color augmentation to DINO.
  • Vision Transformer Based Architectures: Vision Transformer models scale through data, architecture, or hierarchical design to capture fine-grained, local, global, and multiscale pathological features.Phikon-v2 used 460 million pathology tiles from over 100 cohorts; BEPH used over 11 million TCGA tiles and outperformed DINO and ResNet in whole-slide classification and survival prediction.
  • Hybrid and Multitask Architectures: Hybrid and multitask architectures combine convolutional and transformer networks or jointly train classification, segmentation, and detection to learn richer, potentially more generalizable representations.TissueConcepts uses transformer- and convolution-based architectures across multiple tasks, while CTransPath uses a ConvNet and Swin Transformer with semantically relevant contrastive learning.
  • Applications and Future Directions: Foundation-model embeddings support downstream capabilities including zero-shot classification, rare-cancer retrieval, diagnostic assistance, and personalized-medicine applications.The review identifies standardized benchmarks, interpretability, explainability, multimodal learning, and efficient clinical validation as continuing priorities.

3.4. GPU Needs for Pretraining

Pretraining large-scale single- and multimodality pathology foundation models requires substantial GPU resources. This computational burden can make such work inaccessible to academic research groups and laboratories.

  • GPU Needs for Pretraining: Several GPU computers are required to pretrain large-scale single- and multimodality pathology foundation models, creating an access barrier for academic groups.Table 6 provides examples of GPU-resource needs.

3.5. Aggregation

Aggregation converts tile-level information into slide-level representations, but gigapixel whole-slide images make this computationally difficult. Foundation models use varied aggregation strategies, and many still lack slide-level aggregation.

  • Aggregation Challenges: Aggregation remains a major challenge because gigapixel whole-slide images require tile-level representations to be combined into slide-level predictions under hardware and software constraints.WSIs can reach 150,000 square pixels.
  • Aggregation Strategies: Seven foundation models omit aggregation during training and evaluation and instead make predictions on regions of interest within whole-slide images.The listed models include PathChat, PathAsst, PathMMU, RudolfV, Virchow 2, Virchow 2G, and PLIP.
  • Aggregation Strategies: Nine foundation models develop new aggregation methodologies for slide-level representation learning.Examples include mSTAR, PathAlign, PRISM, Prov-GigaPath, SlideChat, CHIEF, COBRA, and TITAN, with THREADS also described among the approaches.
  • Multimodal Aggregation: Multimodal aggregation models align slide representations with molecular data, generate clinically evaluated text, or achieve strong cancer detection and subtyping performance.PathAlign text was rated clinically accurate for 78% of cases, while PRISM reported AUC scores exceeding 0.9 for cancer detection and subtyping.
  • Aggregation Architectures: Vision-language and slide-level models use architectures including LongNet sparse attention, weakly supervised attention pooling, Mamba-2, gated attention, and staged WSI-language alignment.TITAN aligns WSI embeddings with synthetic captions and medical reports across a three-stage pipeline.
  • Challenges and Future Directions: Most foundation models do not yet incorporate aggregation, while supported models use inconsistent approaches that produce variable performance across pathology tasks.The review identifies aggregation integration, multimodal integration, and hardware-software advances as priorities for more efficient and scalable systems.
  • Challenges and Future Directions: Integrating and evaluating aggregation techniques is presented as necessary for advancing pathology foundation models toward clinical-grade performance.The review describes mSTAR, PathAlign, PRISM, and Prov-GigaPath as examples of progress while noting substantial room for growth.

4. Evaluation

Foundation-model evaluation must track both foundational capabilities and task-specific emergent abilities. The review therefore distinguishes intrinsic from extrinsic evaluation and emphasizes accounting for the resources required for adaptation.

  • Evaluation Framework: Foundation-model evaluation is needed to track progress, foster understanding, and document models as they are adapted across numerous applications.Adaptation to specific tasks can produce emergent abilities and creates distinctive evaluation challenges.
  • Intrinsic and Extrinsic Evaluation: Intrinsic evaluation assesses a foundation model’s capabilities independently of a specific task, whereas extrinsic evaluation assesses adapted models on task-specific abilities.Extrinsic evaluation includes task-specific evaluation through meta-benchmarks.
  • Resource-Aware Evaluation: Evaluation should account for resources used during training and adaptation, including data needed to select adaptation methods and access constraints.This broader accounting is intended to provide insight into suitable adaptation strategies for different contexts.

4.1. Adaptation

Adaptation refines foundation models for specialized pathology tasks because general-purpose models may lack deep domain knowledge and contextual sensitivity. The section also highlights continual learning as a future direction, while noting resource costs and catastrophic forgetting risks.

  • Adaptation: Adaptation adjusts broadly trained foundation models to improve precision and relevance on specialized pathology tasks.It narrows general capabilities toward targeted downstream applications.
  • Adaptation: Task-specific adaptation can improve downstream performance and require fewer resources than training a model from scratch.The section presents adaptation as a bridge between broad generalization and specialized effectiveness.
  • Adaptation: Prompting, fine-tuning, continual learning, and resource-efficient low-storage methods provide different adaptation strategies.Prompting changes instructions, whereas fine-tuning updates parameters with domain-specific data.
  • Adaptation: Adaptation improves task performance but requires additional resources and reflects the limits of equal effectiveness across all domains.This creates a practical trade-off between generalization, specialization, efficiency, and usability.
  • Adaptation: Continual learning could reduce retraining needs while keeping models current, but catastrophic forgetting, misalignment, and feedback loops require caution.Memory mechanisms and parameter-update innovations are being explored to address these risks.

4.2. Evaluation of pathology foundation models

Pathology foundation models are evaluated across conventional, advanced, and unique tasks using downstream, intrinsic, retrieval, zero-shot, multimodal, and interactive assessments. Reported results show broad capabilities, but evaluation gaps remain around generalist competence, bias, and clinical nuance.

  • Evaluation of pathology foundation models: Extrinsic evaluation adapts pathology foundation models to specific downstream tasks, while broader assessments include intrinsic representation metrics and retrieval benchmarks.Kaiko-ai combines label-free RankMe and ODCorr with downstream linear probing across tile- and slide-level tasks.
  • Evaluation of image only models: RodulfV improved tumor microenvironment cell-classification performance by 10.8% on average over the closest contender across broad disease and biomarker evaluations.Its evaluation also included histological and molecular prediction benchmarks and reference-case search.
  • Evaluation of image only models: TissueConcepts achieved comparable performance to self-supervised foundation models using 6% of the data and resources.Its encoder training emitted an estimated 18.91 kg of CO2 versus up to 2004 kg for a comparable self-supervised setup.
  • Evaluation of image only models: Image-only models were evaluated on hierarchical cancer classification, rare-cancer prediction, pan-cancer biomarker prediction, and diverse slide-level tasks.These tasks span classification, segmentation, retrieval, genomic characterization, and survival-related prediction.
  • Evaluation of image and text aligned models: Image-text models add zero-shot classification, cross-modal retrieval, and label-free tissue segmentation, but challenging zero-shot performance remains inferior to supervised counterparts.These evaluations use shared image-text representations and predetermined class prompts.
  • Evaluation of image and text aligned models: MUSK outperformed seven other foundation models in bidirectional cross-modal retrieval and showed strong results across classification, retrieval, biomarker, relapse, prognosis, response, and survival tasks.Its evaluations covered 12 datasets for several image-encoder capabilities and 16 major cancer types for pan-cancer prognosis.
  • Evaluation of multimodal models: THREADS demonstrated transferability across 54 tasks, with superior fine-tuning performance against CHIEF on all 54 tasks and against GIGAPATH on 40 of 54.It was evaluated across clinical, molecular, treatment-response, survival, and retrieval tasks using in-house and public datasets.
  • Evaluation of AI Copilots: AI-copilot evaluation combines benchmark questions with interactive, multi-turn conversations assessing image description, diagnosis, prognosis, treatment, testing, and molecular reasoning.PathChat was evaluated on multiple-choice questions covering 54 diagnoses from 11 pathology practices and organ sites.

4.3. Computational Pathology Advance

Foundation models have expanded computational pathology from established diagnostic tasks to multimodal, cross-tissue, molecular, reporting, and interactive assistance applications. Across these uses, models support cancer detection, subtype and tissue analysis, biomarker and mutation prediction, report generation, and complex diagnostic interpretation.

  • Cancer Detection and Subtyping: Foundation models address cancer detection and subtyping across common, rare, and multiple cancer types, including tissue-agnostic detection and slide-level classification.UNI handles up to 108 cancer types, while TITAN performs slide-level classification across 46 OncoTree classes and additional pathology attributes.
  • Multimodal and Zero-Shot Applications: Multimodal foundation models enable zero-shot cancer subtyping, biomarker prediction, image-text retrieval, and slide-level analysis without requiring additional labels in some applications.PathGen-1.6M maintained an average median zero-shot accuracy of 70.2% across BRCA, NSCLC, and RCC subtyping tasks.
  • Tissue Subtypes and Composition: Pathology foundation models support tissue subtype classification, tumor-infiltrating lymphocyte detection, nuclei segmentation, and immune or stromal cell classification.Reported applications include dermatologic tissue reports, pan-cancer lymphocyte detection, and cell-level classification of fibroblasts, granulocytes, macrophages, and plasma cells.
  • Report Generation and Diagnostic Assistance: Foundation models generate clinical reports and provide interactive diagnostic assistance through complex queries, zero-shot classification, multimodal reasoning, and human-in-the-loop workflows.PathChat supports multiple-choice questions, multi-turn conversation, descriptions, guardrails, and open-ended queries, with stronger diagnostic accuracy when images and clinical context are available.
  • Broader Clinical Applications: Additional applications include rare disease classification, multiple-stain analysis, survival and metastatic analysis, and interpretation of complex pathology data at cellular resolution.Reported systems handle rare brain tumors, IHC evaluation, multimodal survival analysis, PD-L1 detection, liquid-based cytology generation, and positive-cell counting.

4.4. Benchmarking Pathology Foundation Models

Benchmarking studies evaluate pathology foundation models across slide analysis, pretraining, retrieval, consistency, and clinically relevant tasks. They show strong but uneven performance, with domain-specific pretraining and data diversity often important, while specialized clinical tasks still require adaptation and broader evaluation.

  • Benchmarking Scope: Benchmarking studies compare foundation models across slide-level classification, aggregation methods, pretraining strategies, multiple-instance learning, and cross-dataset adaptation.The reviewed benchmarks span open-source pathology platforms, end-to-end high-resolution slide modeling, six foundation models across five WSI tasks, and 14 datasets covering five organs.
  • Cross-Task Performance: Domain-specific histological foundation models consistently outperformed ImageNet-based models, but no single model excelled across all tasks.Spatially aware aggregation improved ImageNet-pretrained models but not domain-specific foundation models, and expected advantages were not uniformly observed.
  • Pretraining and Scaling: DINO- and DINOv2-based models generally performed better in disease and biomarker detection, while pretraining dataset choice mattered more than model size for some tasks.ImageNet-based encoders and CTransPath underperformed, and larger models did not consistently improve disease detection or biomarker prediction despite higher computational costs.
  • Multimodal and Ensemble Comparisons: Multimodal and ensemble strategies improved benchmark performance, with CONCH outperforming vision-only models on 42% of tasks and ensembles surpassing CONCH on 66%.The findings also indicate that models trained on distinct cohorts learn complementary features and that data diversity can be more beneficial than data volume.
  • Critical Benchmarking Findings: Generalist foundation models often need retraining or extensive fine-tuning for specialized applications and do not yet handle the full range of clinical tasks reliably.Giga-SSL achieved an AUROC of 0.75 or below for MSI prediction, while other benchmarks reported variable biomarker outcomes and task-specific training requirements.
  • Evaluation Gaps: Reliable clinical deployment requires evaluations of robustness, generalization, multimodal safety, and fairness using benchmarks that enable direct and fair comparison.Existing studies often construct local benchmarks, limiting direct comparison, while interactive systems also require assessment of privacy, safety, explainability, and ethical deployment.

4.5. Societal Impact

Foundation models and AI copilots may reshape pathology research, diagnostics, and healthcare delivery through real-time assistance and biomedical image analysis. Their societal impact is constrained by adoption, privacy, safety, explainability, and risks of bias that can produce inequitable outcomes.

  • Potential Societal Impact: Foundation models may drive a shift in computational pathology by supporting new clinical diagnostic and research applications with potentially more equitable and efficient healthcare outcomes.The paper frames these applications as a broader societal impact of foundation models rather than a completed clinical transformation.
  • AI Copilots: Research-use copilots are being developed to provide real-time insights for pathologists, oncologists, clinical teams, and biomedical image analysis.Paige Alba integrates OmniScreen and Virchow 2, while Judith combines UNI, CONCH, and PathChat for scientific discovery.
  • Risks and Equity: AI agents and copilots can produce intrinsic, adaptation, representational, and extrinsic harms that affect individuals, population groups, and subgroups.The cited risks include misrepresentation, underrepresentation, overrepresentation, and performance disparities linked to training data, adaptation data, modeler diversity, architectures, objectives, and downstream use.

5. Adaptation Studies on Foundation Models

Adaptation studies show that foundation models can support specialized pathology tasks across segmentation, classification, survival analysis, treatment-response prediction, and multimodal representation learning. However, performance and generality remain uneven, with important gaps in pathology-specific pretraining and multi-level WSI modeling.

  • Adaptation studies: Foundation-model adaptations support pathology tasks including segmentation, survival analysis, treatment-response prediction, and multimodal WSI representation learning.Approaches include vision-language priors, MIL, contrastive alignment with gene expression, and SAM-based segmentation.
  • Multimodal adaptations: Multimodal approaches improve pathology representation learning by combining visual data with textual reasoning or gene-expression profiles, particularly in data-scarce settings.TANGLE reports superior few-shot performance across independent breast, lung, and liver WSI datasets.
  • Efficient adaptation: 94% and 85% reductions in computational time and storage, respectively, are reported for CAMP, supporting more practical large-scale pathology analysis.CAMP and GPC provide unified frameworks for cancer grading, detection, and subtyping.
  • Findings and critical remarks: SAM-based models improve pathology segmentation and classification, but SAM performs inconsistently on dense instance segmentation and pathology lacks a comprehensive foundation model for multi-level predictive aggregation.The review identifies pathology-specific pretraining on millions of slides and broader multi-task modeling as open opportunities.
  • SAM-based adaptations: 29.27% higher Dice scores than fine-tuned SAM were achieved by SegAnyPath on external datasets after training on diverse organs, magnifications, stains, and segmentation masks.SegAnyPath uses stain augmentation, self-distillation, a task-guided mixture-of-experts decoder, and masked-autoencoder pretraining.

6. Clinical-grade Evaluation of Pathology Foundation Models

Clinical-grade evaluation shows that pathology foundation models can be competitive, but their performance varies across tasks and cohorts. Specialized models may still outperform general-purpose models, while colon biopsy pre-screening remains insufficiently evaluated and broader benchmarking is needed.

  • Prostate diagnosis: Virchow achieved comparable performance to Paige Prostate, but its slightly lower AUC highlights a remaining gap between general-purpose and task-specific clinical models.The comparison motivates further investigation into fine-tuning while preserving generalist capabilities.
  • MSI prediction: 92–95% sensitivity enabled MSIntuit CRC to rule out approximately 40% of microsatellite-stable patients from further testing.On two external cohorts, it reported AUROCs of 0.88 and 0.87 with sensitivities of 0.98 and 0.96, respectively.
  • MSI prediction: 0.66 to 0.978 AUROCs on the PAIP cohort demonstrate substantial variability among foundation models for colorectal cancer MSI prediction.The review links discrepancies to generalization challenges, clinical-dataset variability, and limitations in MSI estimation methods.
  • Colon biopsy pre-screening: At 0.99 sensitivity, CAIMAN and IGUANA achieved cross-validation specificities of 0.56 and 0.55 for colon biopsy pre-screening.Foundation-model evaluation on this task remains limited, with UNI, REMEDIS, and CTransPath reporting balanced accuracies of 65%, 52%, and 39%, respectively, on colorectal cancer screening.
  • Findings and critical remarks: Comprehensive benchmarking is needed because foundation-model performance remains variable across tasks, cohorts, and clinical settings.The review emphasizes model refinement, generalization evaluation, and clinically appropriate metrics before clinical-grade adoption.

7. Foundation Pathology Models and Grand Challenges

Foundation pathology models could improve diagnostic efficiency and multimodal analysis, but clinical impact is constrained by data quality, privacy, computation, interpretability, generalization, workflow integration, and ethical concerns. Standardized evaluation and trustworthy deployment are central to addressing these challenges.

  • Data and generalization: WSI variability across staining, scanning, institutions, and patient populations makes robust generalization difficult and complicates clinical scaling.The review identifies sample preparation, scanner differences, and institutional variation as major sources of deployment difficulty.
  • Data acquisition and quality: Expensive expert annotation, inconsistent WSI standardization, and limited pathologist reporting consensus slow development of robust computational pathology models.The challenge spans staining, scanner settings, data acquisition, and agreement on labels or reports.
  • Privacy and governance: Privacy regulations and uncertain data ownership make cross-institutional clinical data sharing difficult for collaborative AI development.HIPAA and GDPR protect patient information but create practical barriers to pooling data across institutions.
  • Computational infrastructure: Gigapixel WSI processing requires substantial computing resources, while real-time low-latency analysis remains difficult for institutions without high-performance computing access.Cloud computing can help, but does not eliminate the infrastructure challenge for clinical decision-making.
  • Interpretability: Interpretability remains a major adoption barrier because healthcare deployment requires accountability, transparency, and regulatory explainability.Mechanistic interpretability studies can connect cell and tissue morphology representations with gene expression, offering one route toward more interpretable models.
  • Evaluation and adoption: Standardized benchmarks should assess robustness and generalization under real-world variability to improve clinical applicability and trustworthiness.The review points to GLUE, SuperGLUE, and few-shot evaluation as frameworks that could inform pathology foundation-model assessment.
  • Clinical integration and ethics: Workflow integration is limited by pathologist hesitation, infrastructure requirements, algorithmic bias, and unresolved accountability for AI errors.Transparency, accessibility, and targeted applications are presented as factors that may support adoption without removing these concerns.
  • Findings and critical remarks: Large adaptable foundation models show promise for predictive, contrastive, and generative pathology applications, but generalization, privacy, and interpretability remain unresolved obstacles.The review frames these issues as central to achieving equitable and efficient clinical impact.
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