AI-driven diagnosis of pneumococcal infections in children-a call for primary-care focused and a coherent evidence base.

Introduction: Despite widespread pneumococcal vaccination programmes, the disease remains a leading cause of paediatric morbidity and mortality globally. Persistent barriers, including limited vaccine access and coverage, cold-chain constraints, and delayed or inaccurate diagnosis, disproportionately affect rural communities, primary healthcare settings, and populations with low socioeconomic status. These same settings typically lack the radiological expertise required for timely diagnosis. Problem: Traditionally, diagnosis is done through chest X-ray interpretation by trained paediatricians or radiologists, who are rarely available in remote or resource-limited facilities. Artificial intelligence (AI)-aided diagnostic tools offer a potential pathway to address these gaps, enabling timely and accurate diagnosis, improved access, appropriate treatment, reduced human suffering, and potential cost-savings for healthcare providers and families. Evidence gap: To date, very few studies have evaluated AI-driven diagnostic tools specifically for pneumococcal infections focusing on children. Additionally, existing research is characterised by multiple shortcomings such as limited population diversity, small sample sizes, lack of external validation, absence of standardised reporting, and a hospital-centric focus that overlooks primary and community care and low-resource settings. Proposed approach: Given the many unanswered questions, we propose a transparent comprehensive systematic scoping review to identify and synthesise relevant existing and emerging body of literature in this field with a focus on children. The review will map the extent, range, and nature of available evidence, identify critical gaps, and assess the scope of validation and implementation research to date. It will also provide a framework for the economic analysis. Conclusion: Future research must move beyond isolated proof-of-concept studies toward a coherent, transparent, collaborative, and evidence-driven agenda. This agenda should include rigorous economic evaluations to fully understand resource implications for healthcare providers, health systems, and societies, particularly in low-resource settings where the potential impact of AI-driven diagnostics could be greatest.