FEDERATED CLINICAL NATURAL LANGUAGE PROCESSING FOR EARLY DISEASE PREDICTION IN CLOUD ENVIRONMENTS: A COMPREHENSIVE REVIEW

Abstract

The digitalization of healthcare has produced huge databases of unstructured clinical histories, but their use in the prediction of diseases at an early stage is often limited by strict regulations of data privacy and institutional silos. The current paper presents an in-depth discussion of Federated Learning (FL) as a decentralized model of clinical Natural Language Processing (NLP) on the cloud. We outline a multi-layered taxonomy of recent literature that categorizes it in terms of architectural frameworks, methodological developments since Transformers to Large Language Models (LLMs), and cloud-native orchestration approaches. We combine the research findings of high impact to determine the trade-off between the diagnostic utility and regulatory compliance of privacy-preserving mechanisms, including Differential Privacy and Homomorphic Encryption. We find that it has been successfully used in clinical applications in chronic and rare disease prediction and that important open challenges, such as explainability, communication overhead, and multi-cloud scalability are identified. The review concludes that convergence of FL and cloud-native NLP are necessary to construct secure, scalable, and cross-institutional predictive models that can be used to greatly improve patient outcomes without undermining data sovereignty.