Descripción del proyecto
Datos masivos e inteligencia artificial para unas innovaciones médicas seguras
Los datos masivos y la inteligencia artificial (IA) propician nuevas oportunidades para la mejora de la asistencia sanitaria. Sin embargo, también ocultan riesgos para la seguridad de datos clínicos sensibles almacenados en la infraestructura crítica de TIC de la asistencia sanitaria. El proyecto FeatureCloud, financiado con fondos europeos, propone un concepto innovador de seguridad desde el diseño que tiene como objetivo reducir la posibilidad de ciberdelitos y favorecer iniciativas colaborativas, transfronterizas y seguras de extracción de datos. El concepto se aplicará a un juego de herramientas de «software» que emplea el primer método de privacidad desde la arquitectura del mundo. Las prestaciones básicas de este método son que no se comparten datos sensibles a través de ningún canal de comunicación y que no se almacenan datos en un punto central. FeatureCloud integrará el aprendizaje automático federado con la tecnología de cadena de bloques para aplicar de forma segura la tecnología de IA de próxima generación en las innovaciones médicas.
Objetivo
The digital revolution, in particular big data and artificial intelligence (AI), offer new opportunities to transform healthcare. However, it also harbors risks to the safety of sensitive clinical data stored in critical healthcare ICT infrastructure. In particular data exchange over the internet is perceived insurmountable posing a roadblock hampering big data based medical innovations. FeatureCloud’s transformative security-by-design concept will minimize the cyber-crime potential and enable first secure cross-border collaborative data mining endeavors. FeatureCloud will be implemented into a software toolkit for substantially reducing cyber risks to healthcare infrastructure by employing the world-wide first privacy-by-architecture approach, which has two key characteristics: (1) no sensitive data is communicated through any communication channels, and (2) data is not stored in one central point of attack. Federated machine learning (for privacy-preserving data mining) integrated with blockchain technology (for immutability and management of patient rights) will safely apply next-generation AI technology for medical purposes. Importantly, patients will be given effective means of revoking previously given consent at any time. Our ground-breaking new cloud-AI infrastructure only exchanges learned model representations which are anonymous by default. Collectively, our highly interdisciplinary consortium from IT to medicine covers all aspects of the value chain: assessment of cyber risks, legal considerations and international policies, development of federated AI technology coupled to blockchaining, app store and user interface design, implementation as certifiable prognostic medical devices, evaluation and translation into clinical practice, commercial exploitation, as well as dissemination and patient trust maximization. FeatureCloud’s goals are bold, necessary, achievable, and paving the way for a socially agreeable big data era of the Medicine 4.0 age.
Ámbito científico
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesdata sciencedata mining
- social sciencespolitical sciencespolitical transitionsrevolutions
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringdigital electronics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SC1-FA-DTS-2018-1
Régimen de financiación
RIA - Research and Innovation actionCoordinador
20148 Hamburg
Alemania