The rapid development of artificial intelligence (AI) in the medical field has opened new possibilities to enhance 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 these advances; 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.

Digital twin technology provides patients with the opportunity to use individualized health trajectories by creating a virtual model with the help of AI (Sadeghi et al., 2024). Such tools are particularly applicable to media-based interventions and digital health campaigns 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 Research Topic presents a number of representative studies on various but complementary clinical areas. Among the contributions is 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 modeling 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 underpinning transparent and personalized therapeutic decision-making processes (Khoshfekr Rudsari et al., 2025).

Moreover, a computer-aided diagnosis system based on a digital twin to analyze skin cancers is presented (Sampath et al.), where an explainable meta-learning mechanism can enhance the efficiency of diagnostics, but the clinical reliability does not decrease. In addition to these centralized solutions, a federated learning-based ensemble framework based on explainable AI is introduced to diagnose lung diseases in real-time (Durga et al.), which fulfills the key requirements regarding data privacy, decentralized learning, and explainability in large-scale medical imaging systems. Taken together, these papers illustrate the effectiveness of explainable AI and the digital twin technologies in various contexts of health conditions, data types, and implementation environments, further supporting the main idea of the present Research Topic: how to promote trustworthy, transparent, and human-centered AI-based healthcare interventions.

Health interventions developed via digital media have included interactive mobile apps, social media messages, virtual reality therapy, and online educational information. The strategies also reach a wide population and can be dynamically modified to convey the key message, yet their potential depends on personalization and the trust of the audience. Experience shows that the transparency in the use of data and the logic of decisions can result in a significant increase in the level of engagement: users will be willing to engage in an intervention when they feel informed and assured (Sampath et al.). This highlights the importance of technologically advanced and user-friendly systems. XAI, based on digital twin technology, provides a promising avenue through which these needs can be fulfilled by providing adaptable, interpretable interventions.

To conclude, explainable AI is timely and very effective when paired with digital twin modeling in media-based health interventions. XAI is more transparent and trustworthy as it makes algorithmic decisions explainable, whereas digital twins offer customized and predictive simulations to personalize care. The combination of them allows interventions that are sensitive to the needs of the individuals and explains why they are necessary. This convergence is capable of taking generic outreach digital health efforts to the next level of truly personalized care, which is the purpose of modern medicine. We urge researchers and practitioners to make innovations here at the crossroads. The cross-disciplinary approach and careful review of the results can help the community to make sure that AI-based media interventions can be adaptive, engaging, and credible, and, finally, that they can enhance the state of health for everyone.