Jul 2026· Chinese Medical Journal· 0 citations· 126 references
Medicine
TL;DR
A dual-perspective framework to systematically bridge the gap between algorithmic advances and practical demands of pathological diagnosis and prognosis is proposed, which offers practical guidance for selecting and designing AI solutions tailored to specific clinical goals.
Abstract
ABSTRACT
The adoption of whole-slide imaging is establishing a new paradigm in digital pathology. However, the translation of artificial intelligence (AI) from research to clinical practice faces significant hurdles, largely due to a misalignment between algorithmic advances and the practical demands of pathological diagnosis and prognosis. In this review, we propose a dual-perspective framework to systematically bridge this gap by linking core clinical tasks with cutting-edge deep learning methodologies. We present a comprehensive overview of the field from 2020 to 2025, analyzing how architectures such as convolutional neural networks, vision transformers, and graph neural networks are being adapted for diagnostic classification, tissue segmentation, and prognostic prediction. A key contribution is our novel algorithm-clinical task mapping framework, which offers practical guidance for selecting and designing AI solutions tailored to specific clinical goals. We also highlight emerging trends that minimize reliance on costly annotations-including weakly supervised and self-supervised learning-as well as advances in predicting immunohistochemistry results directly from hematoxylin and eosin-stained slides. Finally, we address critical challenges related to model interpretability, regulatory approval, and multicenter generalization, and outline a future pathway focused on developing integrated, trustworthy, and equitable AI systems that enhance, rather than replace, the expertise of pathologists.
Pathology foundation models are a class of deep neural networks pre-trained on massive pathology image data. These models typically obtain generic representations through self-supervised learning to support a wide range of cancer diagnostic tasks. In recent years, such models have demonstrated significant performance improvements in pan-cancer detection, rare cancer identification, cancer subtype classification, and molecular prediction. This paper focuses on the cancer diagnosis scenario, systematically summarizes the structural design and data resources of representative pathology foundation models, and emphasizes the discussion of patch-level Transformer, graph-based encoders, and visual-language models based on image-text pairing, and analyzes their roles in clinical-related tasks. Furthermore, this paper points out the main challenges currently faced in this field in terms of long-tail generalization, evaluation bias, engineering reproduction threshold, and the reliability of multimodal output, and looks forward to the future development direction of pathology foundation models for reliable clinical deployment.
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
PURPOSE
The advancement of decision support systems for pathology and their implementation in clinical practice have been limited by the necessity for extensive, manually annotated datasets. Self-supervised learning (SSL) automates the extraction and interpretation of histopathological features from unannotated images, facilitating efficient model development without dependence on expert labeling. In this study, we introduce the SSL-HistoNet model that learns disease-relevant morphological representations from histopathological images through self-supervised learning.
MATERIALS AND METHODS
We applied it to WGA-stained skeletal muscle tissues from mouse models of amyotrophic lateral sclerosis (ALS) and Type I diabetes to explore its ability to capture pathological muscle phenotypes in an annotation-free setting. Following pretraining on unlabeled data, the SSL encoder was further integrated with an attention-guided classifier to evaluate its capacity to identify pathological muscle alterations.
RESULTS
SSL-HistoNet achieved a precision of 0.98, a recall of 0.98, and an AUC of 0.98, demonstrating performance comparable to or outperforming state-of-the-art supervised models. Alongside high discriminative performance, exploratory feature analyses demonstrated consistent class-level changes in morphology-related patterns identified through representation learning.
CONCLUSION
These findings highlight the capability of SSL-HistoNet as an annotation-free framework for outlining disease-specific tissue structures, reducing manual labeling demands and mitigating inter- and intra-observer variability in histological processes.
CLINICAL TRIAL NUMBER
Not applicable.
Taymaz Akan, Richa Aishwarya, M. Bhuiyan et al.· BioData Mining· 0 citations
The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems.
Abdul-Mohsen G. Alhejaily, D. Alghamdi· Biomedical Reports· 0 citations
This survey extends beyond traditional and deep learning-based augmentation techniques or deep semi-supervised approaches, by explicitly focusing on medical/clinical imaging modalities, by explicitly focusing on CT, MRI, and X-ray, offering a broader perspective.
Pratiksha Gawas, S. Kamath S.· Multimedia tools and applica...· 0 citations
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.
Lakshmi Sai Anusha Dadi, Pravallika Devi Kommana· International Journal for Re...· 0 citations
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