Patient safety and diagnostic quality are central to modern histopathology practice. Current incident investigation approaches often rely on root cause analysis; however, its routine use as a universal method is insufficient to address the highly variable, cognitive, sociotechnical and multifactorial nature of histopathology errors. In practice, incidents range from simple technical failures to complex system-level events requiring distinct investigative strategies. A key limitation of current practice is the lack of proportionality, where similar methods are applied irrespective of case complexity. In addition, corrective actions frequently target the immediate error without adequately considering the broader system context, potentially increasing workload, introducing new risks or degrading long-term service performance and operational sustainability. In this narrative review, we propose a practical five-stage systems framework for incident management in histopathology. This framework integrates proportional incident classification, structured systems investigation, prospective evaluation of corrective actions to minimise unintended consequences, implementation of system-level improvements and continuous performance monitoring. By selectively applying established human factors and quality improvement methodologies according to incident complexity, the framework shifts incident management from retrospective error investigation towards proactive system optimisation and continuous organisational learning. Ultimately, it provides a practical, scalable approach that improves diagnostic reliability, reduces recurrence, enhances efficiency and ensures that corrective actions are effective, sustainable and aligned with real-world laboratory practice.

Artificial intelligence (AI) has emerged as a promising adjunct in surgical pathology, particularly in the diagnosis of prostate cancer, where variability in interpretation or missed cancer foci can significantly affect patient management. This review provides a concise, practice-oriented overview of the two Food and Drug Administration (FDA)-cleared AI tools for prostate biopsy interpretation: Paige Prostate Detect and Ibex Prostate Detect (formerly Galen Second Read). We examine their regulatory indications, diagnostic performance and integration requirements within digital pathology workflows. Emphasis is placed on real-world implementation considerations, including variation in technical inputs and the level at which data are analysed. We highlight less obvious risks, such as domain shift and the potential for inequitable performance in under-represented patient populations. Trade-offs between sensitivity and specificity, particularly in the context of AI-assisted pathologist assessments, are discussed using data from clinical validation studies. We also consider the variable impact of AI tools depending on the user's expertise, noting enhanced diagnostic consistency for general pathologists. By highlighting both the opportunities and limitations of integrating AI into routine practice, we aim to provide pathologists with a pragmatic understanding of how these systems may influence diagnostic workflows and to emphasise that FDA clearance must be complemented by local validation as well as ongoing performance monitoring to ensure safe and equitable deployment.

Artificial intelligence (AI) tools for digital pathology have crossed the threshold from pilot project to clinical deployment, with regulatory approvals, commercial products and prospective trials now accumulating across multiple tumour types. However, global-scale implementation demands careful evaluation of both opportunities and challenges. Traditional histopathology faces mounting pressures including interobserver variability and escalating workloads. At the same time, molecular diagnostics have become increasingly complex, while core workflows have remained fundamentally unchanged for decades. Digital pathology infrastructure and AI algorithms promise solutions through enhanced diagnostic accuracy, improved efficiency, democratised access to expertise and unprecedented research capabilities. AI has already demonstrated clinical value across multiple subspecialties, including prostate, breast and colorectal cancer, with applications spanning automated biomarker quantification, molecular subtype prediction and prognostic modelling. Systematic reviews also identify important limitations that temper enthusiasm for AI adoption. These include heterogeneity in study design, publication bias favouring positive results, limited generalisability across diverse populations and practice settings, concentration of research in narrow subspecialties and insufficient external validation. Critical implementation barriers include substantial infrastructure requirements, concerns around data privacy and security, gaps in regulatory oversight and the risk that algorithmic bias may exacerbate health disparities. Professional bodies advocate for responsible deployment guided by evidence-based standards. Key priorities include rigorous validation, workforce training, workflow integration and continuous performance monitoring. Making transformation equitable, safe and effective requires balancing innovation with accountability. AI should augment rather than replace pathologist expertise and be supported by robust governance frameworks that prioritise patient safety and diagnostic quality over commercial consideration.