Bridging the digital pathology divide: reciprocal learning between high-resource and low-resource settings

Le résumé fourni par la source

Digital pathology (DP) represents a major advance in global health, integrating digital whole-slide imaging, telepathology and clinical-grade artificial intelligence into cohesive workflows that can improve diagnostic accuracy and educational access. Realising these gains, however, depends on local infrastructure, governance, reimbursement and workforce capacity. The COVID-19 pandemic accelerated DP uptake in some settings, but adoption remains highly uneven between high-resource settings (HRS) and low-resource settings (LRS), reflecting unequal infrastructure, workforce, policy and financing. This analysis contends that reciprocal innovation, a model of ongoing two-way learning between HRS and LRS that is distinct from one-way technology transfer, can accelerate DP adoption by leveraging the strengths of each environment. Drawing on examples from HRS and LRS, we examine use cases in education and clinical practice, the adoption barriers that persist and policy mechanisms required for sustainable scaling. While HRS contribute regulatory frameworks, validation standards and mentorship, LRS offer innovations in cost efficiency, workflow resilience and open-source adaptation that can inform more sustainable models globally. Neither HRS nor LRS are homogeneous entities. Resource availability also varies substantially within each category: HRS may contain poorly resourced institutions, while some institutions in LRS may have comparatively greater resources but still face broader system-level infrastructure constraints. Ensuring access to those in LRS requires targeted investment, harmonised standards and mutual learning across resource levels. DP has the potential to foster bidirectional partnerships to democratise access to timely, accurate and patient-centred pathology services across all resource settings.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.