Cognitive Healthcare Systems: Designing Intelligent Telehealth Platforms with Microservices, AI, and Cloud

Abstract

Cognitive healthcare systems are an important development in the current telehealth. They would combine artificial intelligence, microservices architecture, and cloud computing to provide scalable healthcare. The previous telehealth systems are monolithic and lack scalability, flexibility, and real-time information processing. To address these issues, contemporary systems are designed as microservices, deploy independently, and are self-contained. The other method to improve telehealth platforms is through AI, which allows predictive analytics, clinical decision support, and patient-centered care. The electronic health records or real-time sensor data through models that can provide actionable information and enhance diagnostic accuracy. Cloud computing provides elastic infrastructure, high availability, and cost-effectiveness. This review paper talks about the architectural design, enabling technologies of cognitive telehealth systems. It also discusses the issue of the privacy of data, the security, interoperability and complexity of the systems. The potential resolutions focus on new trends, such as federated learning, edge computing, and explainable AI. In general, the combination of micro-services, AI, and cloud computing has a strong base of the next-generation telehealth platforms. Also, there are a number of challenges associated with practical AI, cloud, and microservices implementation in healthcare. These are integration to legacy systems, data privacy and compliance problem, high cost of infrastructure and operation and the requirement of skill to operate distributed architectures. Moreover, maintaining reliability, interoperability, and clinical validation of AI models continues to be a major challenge to scale-out applications in healthcare settings.