Nowadays, digital health devices play an active role in our healthcare and even let us cooperate with our physicians to improve our health, preventing health deterioration and real-time response to decrease health costs and increase general health quality for individuals. Patients can have greater access to specialized care if equipped with sensing devices that effectively monitor health status and acknowledge alterations or abnormal events. More and more people are effectively using digital health devices, many of which are classified as medical devices (according to the European Union Regulation (EU) 2017/745 and Regulation (EU) 2017/746), equipped with or sending data to AI systems. Shortly, most clinicians, including speciality doctors, paramedics, and nursing staff, will be using some AI technology. These digitally empowered healthcare solutions provide accelerated case detection, constant surveillance, access, and advanced decision-making while improving the quality of services and personalizing health.However, for digital health technology to be effective, reusable, and universal, not only are there insufficient standards yet that allow for the validation of many of these services but also the digital connection with the medical aid and AI processing of health-related data is lacking standardization. While digital health can be underpinned via common standards (like HL7 FHIR) to facilitate communication between devices and systems, we also need standards allowing for the establishment of uniform, transparent, and trustworthy AI processes to be performed on health data, ensuring the compliance of those processes with the existent regulatory framework, well-established Data Privacy safeguards and AI Act compliance. Personal health devices can positively change individual patient outcomes and help make progress in reducing health disparity. However, the data you collect from the devices needs to work with other devices, apps, and platform to communicate with platforms and provide the framework for digital health technology to function in a secure way. Regarding the incorporation of AI in personalized and digital health, in April 2024, the Scientific Advice Mechanism (SAM) delivered his independent scientific advice on AI uptake in research and innovation to the European Commission, where we find the requirement for high-quality standards for AI systems (such as in data, computing, and codes), ensuring fair access for European researchers and innovators and the transparency of public models, helping to increase the trustworthiness of AI and reinforce the replication of research results. Health-related data is personal special data. But it is also relevant data that must be exchanged between different parties (the data owner, medical doctors or clinicians, different clinical entities, caregivers, etc.). More than ever, Public Administration Health entities can benefit from AI processing systems to improve health-related processes and act preventively, sometimes even before the patient acknowledges a health deterioration or a health-related issue, bringing the promise of a huge reduction of the annual health-related costs and improving the general public quality of life. To securely achieve this, a uniformization of technologies that benefits both the industry and the implementation of the products, processes, and services in health is needed. This demands the existence of standards designed to maximize the reliability and security of methods and services for the correct exchange and treatment of health data, namely, agreed-upon norms outlining the best way to perform these data operations between data owners, industry, and government and allowing for accountability. AIH intends to design a standard for the implementation of any end-to-end system to securely collect and process health data using AI and personal mobile (and sensing) devices. It also encompasses the distribution of personal AI Models for the mobile device, possibly updated via federated learning techniques. Such a system functionalities must be enabled by an AI Secure Platform, whose technical characteristics should comply with generally applicable legal frameworks such as the European Union General Data Protection Regulation (GDPR) or the regulation for Artificial Intelligence (Artificial Intelligence Act), to name only the European normative frameworks. To be secure, transparent, and accountable, all the actions performed on health data are to be fully documented and only permitted upon validation of the terms of the licenses associated with that data.

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