DEVELOPMENT OF INFRASTRUCTURE FOR INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO TELEMEDICINE SYSTEMS USING MICROSERVICE ARCHITECTURE

Abstract

This paper presents a microservice architecture for integrating artificial intelligence (AI) models into telemedicine systems, providing scalability, flexibility, and automation of AI model training and deployment. The proposed solution focuses on efficient service interaction through a Message Broker (RabbitMQ/Kafka), eliminating direct dependencies between microservices, improving reliability, and ensuring continuous processing of large volumes of medical data.The HL7 Conversion Service transforms AI predictions into the FHIR HL7 standard, ensuring seamless compatibility with clinical information systems. Additionally, the Model Training Service automates training, versioning, and updating AI models, significantly simplifying their integration and adaptation to new datasets.The designed architecture can be applied for real-time patient monitoring, medical image analysis, patient condition prediction, and personalized medicine. Its implementation reduces query processing time, enhances diagnostic accuracy, and improves treatment efficiency by analyzing vast amounts of medical data. The use of distributed services and automated AI training pipelines ensures system flexibility, allowing it to be easily scaled and adapted to growing data volumes and new research in the healthcare field. The main challenges include complex infrastructure setup, high computational resource requirements, and the need to comply with data security standards, such as HIPAA and GDPR.Thus, the proposed architecture represents an effective approach to deploying intelligent medical platforms, combining powerful machine learning algorithms with a highly reliable microservice structure, fostering the development of innovative healthcare solutions.