Artificial intelligence applied to histological image analysis for diagnostic support: a systematic review of the evidence 2025-2026
Keywords:
Artificial intelligence; histopathology; diagnosis; deep learning; systematic review.Abstract
Artificial intelligence (AI) applied to digital histological images has rapidly evolved toward complex diagnostic tasks. To synthesize the evidence published between 2025 and 2026 on AI applications in diagnostic histopathology. Methodology: Systematic review following PRISMA 2020 in PubMed, Google Scholar, and ScienceDirect, including original human studies using digital histological images for diagnosis, classification, or clinical stratification. Twenty-two studies were included, predominantly in oncologic pathology (gastrointestinal tract, breast, central nervous system). AI architectures evolved from convolutional neural networks toward multi-instance learning, transformers, and foundation models. More than half reported external validation. Main metrics were AUROC, accuracy, and concordance indices. AI in digital pathology shows increasing sophistication, but methodological heterogeneity persists, limiting widespread clinical implementation.
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