Promise and pragmatism of AI in global-scale digital pathology: pan-cancer approaches for clinical practice.

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.