Explainability as the key ingredient for AI adoption in Industry 5.0 settings.

Explainable Artificial Intelligence (XAI) has gained significant attention as a means to address the transparency and interpretability challenges posed by black box AI models. In the context of the manufacturing industry, where complex problems and decision-making processes are widespread, the XMANAI platform emerges as a solution to enable transparent and trustworthy collaboration between humans and machines. By leveraging advancements in XAI and catering the prompt collaboration between data scientists and domain experts, the platform enables the construction of interpretable AI models that offer high transparency without compromising performance. This paper introduces the approach to building the XMANAI platform and highlights its potential to resolve the "transparency paradox" of AI. The platform not only addresses technical challenges related to transparency but also caters to the specific needs of the manufacturing industry, including lifecycle management, security, and trusted sharing of AI assets. The paper provides an overview of the XMANAI platform main functionalities, addressing the challenges faced during the development and presenting the evaluation framework to measure the performance of the delivered XAI solutions. It also demonstrates the benefits of the XMANAI approach in achieving transparency in manufacturing decision-making, fostering trust and collaboration between humans and machines, improving operational efficiency, and optimizing business value.

Analyzing data and data sources towards a unified approach for ensuring end-to-end data and data sources quality in healthcare 4.0.

BACKGROUND AND OBJECTIVE: Healthcare 4.0 is being hailed as the current industrial revolution in the healthcare domain, dealing with billions of heterogeneous IoT data sources that are connected over the Internet and aim at providing real-time health-related information for citizens and patients. It is of major importance to utilize an automated way to identify the quality levels of these data sources, in order to obtain reliable health data. METHODS: In this manuscript, we demonstrate an innovative mechanism for assessing the quality of various datasets in correlation with the quality of the corresponding data sources. For that purpose, the mechanism follows a 5-stepped approach through which the available data sources are detected, identified and connected to health platforms, where finally their data is gathered. Once the data is obtained, the mechanism cleans it and correlates it with the quality measurements that are captured from each different data source, in order to finally decide whether these data sources are being characterized as qualitative or not, and thus their data is kept for further analysis. RESULTS: The proposed mechanism is evaluated through an experiment using a sample of 18 existing heterogeneous medical data sources. Based on the captured results, we were able to identify a data source of unknown type, recognizing that it was a body weight scale. Afterwards, we were able to find out that the API method that was responsible for gathering data out of this data source was the getMeasurements() method, while combining both the body weight scale's quality and its derived data quality, we could decide that this data source was considered as qualitative enough. CONCLUSIONS: By taking full advantage of capturing the quality of a data source through measuring and correlating both the data source's quality itself and the quality of its derived data, the proposed mechanism provides efficient results, being able to ensure end-to-end both data sources and data quality.

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices.

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices' datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

Data Sources and Gateways: Design and Open Specification.

INTRODUCTION: With With the proliferation of available ICT services, several sensors and health applications have become ubiquitous, while many applications have been developed to detect certain health conditions and early signs of disease. Currently, all these services operate independently, and the available data is heterogeneous with limited value gained from its exploitation. AIM: The Data Sources and Gateways component aims at providing an abstracted and unified API to support the data accumulation from various sources including healthcare organisations, biosensors, laboratories and mobile applications. Meanwhile it tackles connectivity and communication issues with such information sources. METHODS: The CrowdHEALTH Data Sources and Gateways Service incorporates four main services: The Configuration Service, The DB Connection Handling Service, The File Parsing Service and The RESTful Client Service. RESULTS: The initial version of the component design has built upon the requirements collected from the use case participants acting also as data providers. CONCLUSION: These four services presented in this paper guide the implementation of the first version of the Data Sources and Gateways component software prototype. The Data Sources and Gateways component remains to be evaluated within the context of the project and be enriched in order to meet additional end user needs.

Using digital watermarking to enhance security in wireless medical image transmission.

OBJECTIVES: During the last few years, wireless networks have been increasingly used both inside hospitals and in patients' homes to transmit medical information. In general, wireless networks suffer from decreased security. However, digital watermarking can be used to secure medical information. In this study, we focused on combining wireless transmission and digital watermarking technologies to better secure the transmission of medical images within and outside the hospital. METHODS: We utilized an integrated system comprising the wireless network and the digital watermarking module to conduct a series of tests. RESULTS: The test results were evaluated by medical consultants. They concluded that the images suffered no visible quality degradation and maintained their diagnostic integrity. DISCUSSION: The proposed integrated system presented reasonable stability, and its performance was comparable to that of a fixed network. This system can enhance security during the transmission of medical images through a wireless channel.

Use of 3G mobile phone links for teleconsultation between a moving ambulance and a hospital base station.

We developed a mobile teleconsultation system based on third-generation mobile phone links. The system comprised a laptop computer and a digital camcorder. It was installed inside an ambulance to allow video-conferencing between the moving vehicle and a doctor at a base station. In addition to video and voice, high-quality still images could also be transmitted. A series of 17 trial runs with real ambulance patients was conducted in the city of Athens. In general, the videoconferencing sessions produced relatively clear video. The bandwidth was high enough for a satisfactory video of 10-15 frames/s. During a total testing period of 23 h and in an area of about 180 km2, there were nine instances of signal loss, amounting to a total of 17 min. The general opinion formed by the doctors was that the system produced good results. All initial diagnoses made using the system agreed with the final diagnoses of the patients. The study showed that the mobile system could reduce the time before an ambulance patient is seen by a doctor.

Digital watermarking in telemedicine applications--towards enhanced data security and accessibility.

Implementing telemedical solutions has become a trend amongst the various research teams at an international level. Yet, contemporary information access and distribution technologies raise critical issues that urgently need to be addressed, especially those related to security. The paper suggests the use of watermarking in telemedical applications in order to enhance security of the transmitted sensitive medical data, familiarizes the users with a telemedical system and a watermarking module that have already been developed, and proposes an architecture that will enable the integration of the two systems, taking into account a variety of use cases and application scenarios.

Ambulance 3G.

Minimising the time required for a patient to receive primary care has always been the concern of the Accidents and Emergency units. Ambulances are usually the first to arrive on the scene and to administer first aid. However, as the time that it takes to transfer the patient to the hospital increases, so does the fatality rate. In this paper, a mobile teleconsultation system is presented, based primarily on third generation mobile links and on Wi-Fi hotspots around a city. This system can be installed inside an ambulance and will permit high-resolution videoconferencing between the moving vehicle and a doctor or a consultant within a base station (usually a hospital). In addition to video and voice, high quality still images and screenshots from medical equipment can also be sent. The test was carried out in Athens, Greece where a 3G system was recently deployed by Vodafone. The results show that the system can perform satisfactory in most conditions and can effectively increase the patient's quality of service, while having a modest cost.

3G Networks in Emergency Telemedicine - An In-Depth Evaluation & Analysis.

The evolution of telecommunications technologies in connection with the robustness and the fidelity these new systems provide, have opened up many new horizons as regards the provision of healthcare and the quality of service from the side of the experts to that of the patients. The purpose of this paper is to evaluate the third generation telecommunications systems that are only recently being deployed in Europe, as well as argue on why a transition from 2G and 2.5G to 3G telecommunications systems could prove to be crucial, especially in relation to emergency telemedicine. The experimental results of the use of these systems are analyzed, the implementation of a tele-consultation unit is presented and their exploitation capabilities are explored.