Generative AI-Based Clinical Decision Support for Personalized Medicine and Remote Healthcare
DOI:
https://doi.org/10.64137/31079377/IJMSD-V2I2P104Keywords:
Generative Artificial Intelligence, Clinical Decision Support Systems, Personalized Medicine, Remote Patient Monitoring, Large Language Models, Medical InformaticsAbstract
The convergence of digital health ecosystems and advanced computational intelligence has paved the way for next-generation clinical decision support systems (CDSSs). Traditional rule-based and predictive machine learning models are often constrained by their inability to contextualize multimodal, unstructured, and highly dynamic patient data. This research article explores the paradigm shift brought about by Generative Artificial Intelligence (GenAI), specifically Large Language Models (LLMs) and Generative Adversarial Networks (GANs), in clinical decision support for personalized medicine and remote healthcare delivery. We investigate the structural architecture, methodology, operational performance, and implementation challenges of generative frameworks capable of synthesizing longitudinal electronic health records (EHRs), real-time continuous physiological data stream telemetry from wearable devices, and high-dimensional genomic arrays. Through extensive qualitative and quantitative architectural comparative analysis, this paper outlines how generative models synthesize clinical reasoning, automate clinical documentation, optimize patient-centric therapeutics, and predict adverse health transitions before clinical manifestation. Concurrently, we evaluate critical structural bottlenecks including algorithmic hallucinations, privacy protection limitations, lack of semantic interoperability, and the imperative for explainable artificial intelligence (XAI). Finally, this study provides an original architectural framework for implementing a reliable, secure, and context-aware generative CDSS within clinical workflows, establishing benchmarks for future clinical trials and multi-institutional deployments.
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