Abstract
Abstract The application of artificial intelligence (AI) in the areas of health, care and social participation offers great opportunities but also involves great challenges. Extensive regulatory, ethical and data-security related requirements exist for data recording, storage and processing of respective personalized and patient-related data. “Artificial Intelligence as a Service” (AIaaS) is pushed for consumer applications by global players, which implies data storage on external database server. However, the available solutions do not meet the requirements. Moreover, small and medium-sized enterprises (SMEs) in the field of healthcare fear the loss of data sovereignty and information outflow. In this paper, we propose a secure and resource-efficient approach by embedding AI directly close to the sensor in combination with secure and distributed data processing on local server or certified “Trusted Data Center”. For this purpose, we have developed the Artificial Intelligence for Embedded Systems (AIfES) platform-independent machine learning library in C programming language. It contains a fully configurable deep artificial neural network with feedforward structure. The library can be run directly on a microcontroller and even allows to train the neural network. Possible healthcare applications include direct (pre-) processing of sensor data, sensor calibration, pattern recognition and classification.
Highlights
In modern technology, the use of artificial intelligence (AI) is becoming increasingly important
Many applications are conceivable in medicine, ranging from AI-based disease diagnosis, through faster drug development, to improved gene processing [1]
Artificial Intelligence for Embedded Systems (AIfES) is compiled as a library for the target platform and integrated into the chosen development environment
Summary
The use of artificial intelligence (AI) is becoming increasingly important. The application of AIfES in the field of healthcare is part of the German Federal Ministry for Economic Affairs and Energy (BMWi) funded project “Care[Ful]KI – Responsible AI Platform for Health, Care and Social Participation” It aims an integrated, on open standards based, legally certain and highly available AI data platform with competence and data pool [8]. Distribution of computing tasks is possible, e.g., by small smart embedded systems taking over data preprocessing and making the results available to a higher-level system, e.g., on local server or certified “Trusted Data Center”. This significantly reduces the amount of data to be transferred. Author Statement Research funding: The work is part of the German Federal Ministry for Economic Affairs and Energy (BMWi) funded project “Care[Ful]KI – Responsible AI Platform for Health, Care and Social Participation”
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