Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.

Clinical Snapshot

Key Findings

• Effect Size: Not quantified - narrative review format

• Primary Outcome: Current AI applications span glomerular segmentation, automated cellularity assessment, dysplasia detection, steatosis quantification, tumor grading, and pattern recognition across multiple biopsy types

• Nnt Or Sensitivity: Performance metrics vary by application but generally show promising research-stage results with limited clinical validation

Clinical Application

Currently limited by technical barriers, regulatory requirements, and need for extensive validation studies Relevant to Australian pathology services, particularly given RCPA quality assurance requirements and TGA oversight of medical devices. Implementation would require alignment with NPAAC standards and consideration of Medicare funding models Pathologists and laboratories processing small tissue biopsies across renal, hepatic, gastrointestinal, hematological, dermatological, and urological specimens

Abstract

Small tissue biopsies, including renal core biopsies, bone marrow trephines, gastrointestinal endoscopic samples, prostate needle cores, liver biopsies, and skin punch or shave specimens, are fundamental to contemporary diagnostic pathology. Their limited volume, focal sampling, and susceptibility to technical artifacts impose distinct interpretive challenges, often requiring semi-quantitative assessment within a restricted architectural context. Although artificial intelligence (AI) has rapidly expanded in digital pathology, most models have been developed using large surgical resection specimens, with comparatively limited attention to small biopsy material. This minireview examines current and emerging applications of AI in small biopsy diagnostics across renal, hepatic, gastrointestinal, hematopathological, dermatopathological, and urological pathology. Reported applications include glomerular segmentation and fibrosis quantification in renal biopsies; automated cellularity, blast detection, and fibrosis grading in bone marrow trephines; dysplasia and microorganism detection in gastrointestinal biopsies; quantitative steatosis and fibrosis assessment in liver samples; tumor detection and grading in prostate cores; and neoplastic pattern recognition in skin specimens. Despite encouraging performance in research settings, substantial barriers to routine clinical implementation remain, including limited dataset size, class imbalance, pre-analytical variability, inter-institutional heterogeneity, and insufficient external validation. We discuss methodological considerations relevant to diagnostic practice, including multi-institutional validation, stain normalization, multimodal integration with histochemistry and ancillary testing, explainability, and regulatory oversight. In the context of small biopsies, AI should be regarded as a quantitative adjunct to morphological interpretation rather than an autonomous diagnostic system. Careful integration within established histopathological workflows is essential to ensure reproducibility, safety, and clinical accountability.

References

1. 1.Alwahaibi, N. (2026). Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology. Histochemistry and Cell Biology. https://doi.org/10.1515/mr-2024-0097