Deployment and Validation of AI-Enabled Screening Tools for Early Disease Detection in Public Health Systems

The global health community faces an enduring challenge: the delayed detection of disease continues to drive preventable morbidity, mortality, and healthcare costs worldwide. Despite significant strides in digitalization and data accessibility, many health systems—especially in low- and middle-income countries—lack scalable and equitable screening mechanisms. Artificial intelligence (AI) and data-driven technologies have shown great potential to address these gaps by automating and accelerating disease screening through analysis of imaging, physiological signals, and electronic health records. However, the translation of these AI innovations from prototypes to sustainable public health interventions remains limited. Barriers such as poor model generalizability, technical infrastructure constraints, ethical concerns, and regulatory uncertainty have impeded real-world implementation.Emerging research in AI deployment highlights the need to bridge the gap between the controlled environment of algorithmic development and the complex dynamics of community and primary care screening. This Research Topic aims to advance understanding and practice at the intersection of AI implementation science and public health screening. It will foreground studies that assess feasibility, scalability, equity, and population-level impact of AI-enabled early detection programs, particularly within resource-constrained or decentralized healthcare systems. By integrating perspectives from data science, health policy, implementation research, and clinical public health, this collection seeks to demonstrate how AI screening can move beyond innovation to system-level adoption.We welcome contributions that emphasize deployment, validation, regulation, and adoption frameworks of AI-based early detection systems. Submissions should focus on pragmatic and evidence-based approaches to incorporating AI into public health practice and demonstrate measurable outcomes, adaptability, and fairness across populations. Work addressing low-resource or community health settings, digital health integration, and ethical deployment will be particularly valued.Areas of interest include, but are not limited to:• Real-world implementation and validation of AI-enabled screening programs in public health systems• Multimodal AI integration (imaging, biosignals, EHR data) for scalable disease screening• Deployment strategies and adaptive algorithms for low-resource and community-based settings• Governance, regulation, and policy frameworks for AI-based diagnostics• Explainable and equitable AI in screening workflows• Implementation science approaches to integrating AI tools into existing health infrastructures• Evaluation of sustainability, usability, and system readiness for AI deployment• Training, workforce capacity-building, and digital literacy for AI adoption in public healthSubmissions that feature field validation, stakeholder collaboration, health economics analyses, or policy recommendations are particularly encouraged. This Research Topic aims to foster real-world progress toward equitable, trustworthy, and sustainable implementation of AI-driven screening in population health. Topic editors: Subraya Krishna Bhat, Mostafa Mohamad, Shaffi Noushath, Anupkumar M Bongale Source: https://www.frontiersin.org/research-topics/81906/deployment-and-validation-of-ai-enabled-screening-tools-for-early-disease-detection-in-public-health-systems