About this Research Topic
In the expanding field of healthcare technology, recent innovations in artificial intelligence (AI) and digital tools are setting new standards for how health services are delivered, particularly through media-driven interventions. Explainable AI (XAI) has emerged as a pivotal element by enhancing transparency, which allows medical professionals and patients to understand, trust, and effectively implement AI-derived insights and decisions. Concurrently, Digital Twin Technology introduces a capability for creating virtual counterparts of physical entities, facilitating highly personalized health management and the dynamic simulation of medical outcomes. The synergy of these technologies holds promise for revolutionizing patient care by tailoring treatments, maximizing engagement, and deepening trust among users.
This Research Topic aims to investigate how the integration of Explainable AI and Digital Twin Technology can transform media-driven health interventions. The goal is to demonstrate how these advanced technologies can amalgamate to deliver personalized, transparent, and efficient healthcare solutions, which are imperative for enhancing patient care and optimizing resource allocation. By focusing on the clear explication of AI decisions and the adaptive capabilities of digital twins, the research will seek to determine effective strategies that influence user behavior, improve engagement, and facilitate the sustained adoption of tech-driven health solutions.
To gather further insights in the realm of Digital Twin Technology in media-driven health interventions, we welcome articles addressing, but not limited to, the following themes:
• Development and application of XAI models in healthcare for transparency and trust.
• Use of digital twin technology for personalized health monitoring and intervention.
• Behavioral modeling and user behavior analytics in health interventions.
• Optimization of media-driven health interventions using XAI and digital twins.
• Enhancing human-media interaction for improved patient engagement and outcomes.
• Real-time adaptive healthcare solutions through AI and digital twin simulations.
• Applications in digital health, chronic disease management, and preventive care.
Each contribution should aim to bridge the gap between theoretical research and practical applications, highlighting innovative ways these technologies can improve healthcare delivery, patient engagement, and clinical outcomes.
This Research Topic aims to investigate how the integration of Explainable AI and Digital Twin Technology can transform media-driven health interventions. The goal is to demonstrate how these advanced technologies can amalgamate to deliver personalized, transparent, and efficient healthcare solutions, which are imperative for enhancing patient care and optimizing resource allocation. By focusing on the clear explication of AI decisions and the adaptive capabilities of digital twins, the research will seek to determine effective strategies that influence user behavior, improve engagement, and facilitate the sustained adoption of tech-driven health solutions.
To gather further insights in the realm of Digital Twin Technology in media-driven health interventions, we welcome articles addressing, but not limited to, the following themes:
• Development and application of XAI models in healthcare for transparency and trust.
• Use of digital twin technology for personalized health monitoring and intervention.
• Behavioral modeling and user behavior analytics in health interventions.
• Optimization of media-driven health interventions using XAI and digital twins.
• Enhancing human-media interaction for improved patient engagement and outcomes.
• Real-time adaptive healthcare solutions through AI and digital twin simulations.
• Applications in digital health, chronic disease management, and preventive care.
Each contribution should aim to bridge the gap between theoretical research and practical applications, highlighting innovative ways these technologies can improve healthcare delivery, patient engagement, and clinical outcomes.
Keywords: Explainable AI (XAI), Digital Twin Technology, Personalized Health Interventions, Patient Engagement, Healthcare Optimization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
