Recent Advances in Automated Mitosis Detection in Digital Pathology: A PRISMA-Guided Systematic Review with...

Background/Objectives: Recent advances in automated mitosis detection in H&E histopathology have expanded AI applications in digital pathology for tumor grading and proliferation assessment. However, reported performance remains difficult to interpret because it is strongly influenced by benchmark selection and heterogeneous evaluation regimes. This review examined how recent methodological advances, dataset context, and evaluation-regime stratification shape performance interpretation. Methods: We conducted a systematic review of peer-reviewed English-language studies published between January 2018 and December 2025. PubMed, Scopus, and IEEE Xplore were searched for mitosis detection, localization, or counting in H&E histopathology. After screening and full-text assessment, 66 studies met the inclusion criteria. We synthesized 60 method papers and considered 6 dataset/challenge descriptor papers separately. Extracted data included task formulation, datasets, evaluation regime, and outcomes. Results: The 60 method papers showed a methodological shift from patch/cell-level classification toward one-stage and two-stage detectors, dense segmentation/heatmap approaches, hybrid pipelines, and emerging robustness-oriented methods. F1 was reported in 59/60 studies, but evaluation practice was heterogeneous: custom hold-out testing predominated, whereas external validation and explicit domain-generalization protocols were uncommon. Evidence remained concentrated in legacy breast benchmarks, while MIDOG-family datasets anchored most robustness-oriented studies. Importantly, dataset names alone were insufficient to determine comparability; for example, "testing on ICPR2014" could refer to organizer-governed hidden-test scoring, post-challenge labels, or author-defined splits of public data. Conclusions: Automated mitosis detection research has diversified rapidly, but cross-study comparability remains limited by inconsistent evaluation and scarce cross-domain testing. Clearer reporting of dataset partitions, evaluation governance, and metrics, with more routine external or domain-held-out evaluation, would strengthen evidence for AI-driven digital pathology and precision oncology.