ReviewComputational and structural biotechnology journal2025
Machine learning methods for histopathological image analysis: Updates in 2024.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.Pediatric surgery international · 2026Pooled it
- A scoping review of TSR analysis in colorectal cancer: implications for automated solutions.Oncology reviews · 2025Pooled it
- [Artificial intelligence and cardiovascular biobanks: Shared technological promise and ethical responsibility].Revista medica del Instituto Mexicano del Seguro Social · 2026Article
- Toward AI-Assisted Precision Diagnostics in Breast Cancer: Source-Group-Preserving Evaluation of Class-Imbalance Strategies for Ordered ER-IHC Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Reducing False Negatives in AI-Based Breast Histopathology: A Clinically Oriented Evaluation of Deep Learning Models Under Domain Shift.Diagnostics (Basel, Switzerland) · 2026Article
- A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization.Scientific reports · 2026Article
- Ligand-Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model.Biomedicines · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs.Nature aging · 2026Article
- Standardized Instance-Level Quantification of CD34-Positive Vessels in Lymph Node Whole-Slide Images Using U-Net.Pathology international · 2026Article
- Review
- Assessing CNNs and LoRA-Fine-Tuned Vision-Language Models for Breast Cancer Histopathology Image Classification.Journal of imaging · 2026Article
- Machine learning and automation methods for the segmentation, classification and quantification of testicular tissue sections.Reproduction & fertility · 2026Article
- Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence.Biomedicines · 2026Article
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Multimodal medical image reconstruction and organ-wise disease classification using a hybrid deep learning-Kalman filtering framework.Frontiers in medicine · 2026Article
- A Systematic Literature Review on Integrated Deep Learning and Multiagent Vision-Language Frameworks for Pathology Image Analysis and Report Generation.Computational and structural biotechnology journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The combination of artificial intelligence and digital pathology has emerged as a transformative force in healthcare and biomedical research. As an update to our 2018 review, this review presents comprehensive analysis of machine learning applications in histopathological image analysis, with focus on the developments since 2018. We highlight significant advances that have expanded the technical capabilities and practical applications of computational pathology. The review examines progress in addressing key challenges in the field as follows: processing of gigapixel whole slide images, insufficient labeled data, multidimensional analysis, domain shifts across institutions, and interpretability of machine learning models. We evaluate emerging trends, such as foundation models and multimodal integration, that are reshaping the field. Overall, our review highlights the potential of machine learning in enhancing both routine pathological analysis and scientific discovery in pathology. By providing this comprehensive overview, this review aims to guide researchers and clinicians in understanding the current state of the pathology image analysis field and its future trajectory.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.