Applied Artificial Intelligence

AI-driven Digital Medicine and Remote Healthcare: Innovative Solutions aligned with SDG 3

The global challenge of achieving Good Health and Well-Being (SDG 3) remains one of humanity's most pressing concerns. The journal Applied Artificial Intelligence invites submissions for a special Article Collection focused on innovative research addressing this Sustainable Development Goal (SDG), with particular emphasis on its specific targets, such as:

• Target 3.1: Reducing the global maternal mortality ratio
• Target 3.2: Ending preventable deaths of new-borns and children under 5 years of age
• Target 3.3: Combating communicable diseases such as AIDS, tuberculosis, malaria, and neglected tropical diseases
• Target 3.4: Reducing premature mortality from non-communicable diseases and promoting mental health and well-being
• Target 3.8: Achieving universal health coverage, including access to essential health-care services and affordable medicines and vaccines

This Article Collection, edited by Editor-In-Chief Professor Marcello Trovati, will be included in Taylor & Francis’ SDG Article Collection Series. This series features Article Collections focused on ground-breaking research addressing global sustainability challenges while accelerating progress toward achieving the UN's SDGs. Papers published within the SDG Article Collection series will benefit from additional promotional activities across Taylor & Francis, increasing the discoverability and visibility of your research.

This Article Collection welcomes Research Articles and Review Articles exploring:

• AI-Driven Diagnostics: Using machine learning to interpret medical imaging, lab results, and clinical data for faster, more accurate diagnoses.
• Precision and Personalized Medicine: Tailoring treatments based on individual genetic, lifestyle, and clinical data—AI helps identify the best course of action for each patient.
• Remote Monitoring and Mobile Health (mHealth): Leveraging wearables and smartphone apps to track patient health in real time, enabling proactive care and early intervention.
• Explainable AI (XAI): Making AI decisions transparent and understandable to clinicians and patients, which is crucial for trust and adoption in healthcare settings.
• Federated Learning: Training AI models across multiple institutions without sharing raw data, preserving patient privacy while improving model robustness.
• Multimodal Data Fusion: Integrating diverse data sources—like electronic health records, imaging, and genomics—into unified systems for comprehensive patient insights.
• Virtual Health Assistants and Chatbots: Supporting mental health, chronic disease management, and patient education through conversational AI tools.
• Ethical and Regulatory Challenges: Addressing concerns around data privacy, algorithmic bias, and the integration of AI into clinical workflows.
• Digital Twins and Predictive Modelling: Creating virtual replicas of patients to simulate treatment outcomes and optimize care strategies.
• AI in Public Health and Population Management: Identifying trends, predicting outbreaks, and optimizing resource allocation at a systems level

Please review the Submission Instructions below prior to submitting your manuscript.