Digital Health and Artificial Intelligence in Health Planning: Innovations, Ethics, and Policy Impact

Éditeurs invités

• Mélanie R. Maia, Institute of Hygiene and Tropical Medicine, IHMT – NOVA University
• Claudia Pernencar, Polytechnic of Leiria
• Elisabeth Schainker, Mass General Brigham for Children
• Wuleta Lemma, Universidade Lusófona
• Mara Guerreiro, Egas Moniz Center for Interdisciplinary Research
• João Gregório, Universidade Lusófona
• Tiago Correia, Institute of Hygiene and Tropical Medicine, IHMT – NOVA University

Synthèse

This special issue focuses on the integration of digital health and artificial intelligence technologies into health planning systems. The call examines how these innovations can transform health system forecasting, resource allocation, and evidence-informed decision-making, while addressing critical questions about supporting human decision-making, ensuring transparency and accountability, and promoting equitable implementation across diverse health systems.

The collection seeks submissions exploring technological applications, ethical considerations including data governance and algorithmic bias, policy implications for health frameworks, and strategies for scaling successful implementations. The scope encompasses workforce planning, service delivery optimization, economic analyses, and cross-cutting equity concerns, welcoming empirical studies, policy analyses, frameworks, and reviews from varied resource settings.

Thèmes proposés

• Technological Innovations in Practice: applications of Digital Health and AI tools in real-world planning and management of health systems
• Ethical Dimensions: implications related to data governance, algorithmic bias, human agency, and social justice
• Policy Implications: AI as a driver or disruptor of health policy frameworks, governance, and regulatory landscapes
• Translational Opportunities: strategies and pathways to scale up successful Digital Health and AI applications from pilot to policy, and from evidence to implementation
• Health Workforce Planning: AI-enabled forecasting supply and demand, managing training and deployment, retention strategies, and workforce equity
• Planning of Health Services and Care Provision: AI-driven modelling for service delivery optimization, triage algorithms, and demand prediction
• Economic Decisions: Cost-effectiveness and cost-utility analyses using AI; decision-support tools for investment planning, resource allocation, and scenario modelling
• Cross-cutting Areas: equity in AI adoption, infrastructure readiness, interoperability challenges, and local/global comparative insights
• Case studies from high-, middle-, and low-resource settings