The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms of diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish the digital representation of a patient’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-rays into five distinct categories of lung diseases, including “normal”, “covid”, “lung_opacity”, “pneumonia”, and “tuberculosis”. The performance of the system was evaluated employing a chest X-ray dataset available in the literature, demonstrating average accuracy of 96.8%, precision of 92%, recall of 97%, and F1-score of 94%. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient’s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights as to personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.

Today’s healthcare facilities require new digital tools to cope with the rapidly increasing demand for technology that can support healthcare operators. The advancement of technology is leading to the pervasive use of IoT devices in daily life, capable of acquiring biomedical and biometric parameters, and providing an opportunity to activate new tools for the medical community. Digital twins (DTs) are a form of technology that are gaining more prominence in these scenarios. Many scientific research papers in the literature are combining artificial intelligence (AI) with DTs. In this work, we propose a case study including a proof of concept based on microservices, the heart DT, for the evaluation of electrocardiogram (ECG) signals by means of an artificial intelligence component. In addition, a higher-level platform is presented and described for the complete management and monitoring of cardiac pathologies. The overall goal is to provide a system that can facilitate the patient–doctor relationship, improve medical treatment times, and reduce costs.

The integration of Artificial Intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in the diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish a digital representation of a patient’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-Rays into five distinct categories of lung diseases, including "Normal," "Covid," "Lung Opacity," "Pneumonia," and "Tuberculosis". The system’s performance was evaluated on a chest X-Ray dataset, demonstrating an impressive average accuracy of 96.6% across all classes. Further tests (prediction) were conducted on the trained network using a third dataset available in the literature and completely unknown to the network, yielding an average accuracy of 98% across three classes. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-Rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient’s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights for personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.

Future healthcare systems will use unconventional communication systems, like Ultrasounds (US), to communicate through human body tissues safely and efficiently. US have been extensively used for medical applications in the last decades, but is well known that performance can be affected by time-varying conditions in the body, making adaptability crucial for body area networks (BAN). Coordinating the behavior of US devices via their Digital Twins (DTs) can enhance adaptation and optimize transmission parameters. Next-generation networks will have DTs of devices cooperating to improve communication reliability. To cope with the intrinsic unreliability of transmissions inside or along the body, and to achieve the required adaptivity, we assume that a learning mechanism based on Multi-Armed Bandit (MAB) can be deployed to optimize data transmission based on the current channel quality. This implies that DTs will iteratively estimate conditions and adapt US transmissions using feedback to optimize metrics like error rate and throughput. Experimental tests on tissue-mimicking phantoms confirm the feasibility of using learning techniques for robust data transmission.

The heterogeneity of Internet of Things (IoT) devices remains a major barrier to scalable deployment, automated onboarding, and cross-platform interoperability. Device-specific protocols, software dependencies, telemetry formats, and runtime constraints often require manual configuration and ad-hoc integration logic. This paper proposes an agentic AI architecture for zero-touch IoT device onboarding, where a large language model is used as an autonomous configuration agent capable of generating device-side software, installing dependencies, validating runtime behavior, and recursively correcting failures until convergence is achieved. The proposed architecture introduces a closed-loop workflow that combines device inspection, AI-based runtime generation, remote deployment, telemetry validation, and self-correction. Interoperability is addressed through a normalized telemetry contract that abstracts device-specific behavior into a common telemetry representation. We evaluate the approach on a real IoT device by benchmarking multiple AI models under clean installation conditions in two campaigns: the original pipeline and a second pipeline assisted by a locally trained semantic context optimizer. The comparison covers convergence time, correction cycles, recursive iterations, and prompt tokens. The optimizer-assisted campaign reduced prompt redundancy and recursive recovery effort while preserving successful onboarding across all tested models. Overall, the results show that agentic AI can support zero-touch onboarding while exposing measurable differences among models in reliability, efficiency, and runtime convergence.