Editorial: Optimizing health outcomes through XAI and digital twins in media interventions

The fast development of artificial intelligence (AI) in the medical field has opened new possibilities in order to enhance the diagnosis, treatment, and prevention. Nonetheless, most of the AI-based systems cannot be easily interpreted, thus restricting the confidence levels of clinicians, patients, and policymakers. Simultaneously, healthcare delivery is becoming more of a product of digital media interventions, including mobile health apps, social media campaigns, and personalized digital platforms. Explainable AI (XAI) has become one of such advances as it is now essential to allow clinicians and patients to trust and understand the decisions made by AI, and Digital Twin technology can provide patient-centered virtual models to reproduce personalized health journeys.Although Digital Twin technology provides patients with the opportunity to use individualized health trajectories by creating a virtual model with the help of AI 1. Such tools are particularly applicable to media-based interventions digital health campaigns and apps provided through an app or social media since they will purport to customize content and explanations to individuals. We as guest editors emphasize that with the help of XAI, the integration of digital twins may make such interventions an individualized, transparent, and efficient healthcare solution and ultimately lead to improved patient engagement and health outcomes.In order to demonstrate the scope and depth of current developments at the intersection of explainable artificial intelligence, digital twins, and health-targeted media interventions, this Special Issue presents a number of representative studies on various but complementary clinical areas. Among the contributions are a suggestible AI-enhanced digital twin system of early detection and predictive treatment of chronic pulmonary abnormalities in young adults in urban areas, which shows how the fusion of multimodal physiological, environmental, and lifestyle data can make the personalized risk stratification of a given population possible. Two other studies are interested in computational modelling of mental health, and give an explainable AI framework and ideas of digital twin to optimize single-dose psilocybin therapy regimens, where interpretable models play an important role in 34 underpinning transparent and personalized decision-making 2. 35 Moreover, a computer aided diagnosis system based on digital twin to analyse skin cancers is presented 36 3, in which an explainable meta-learning mechanism can enhance the efficiency of diagnostics, but 37 the clinical reliability does not decrease. estimated to lower the rehospitalization rates by as much as 25% in specific circumstances. To 81 conclude, it can be noted that the integration of XAI with digital twins is mentioned as a major direction 82 in the future: explainable processes will be used to clear up the predictions of the twin and assist 83 physicians in the interpretation of intricate