Evaluating the Efficacy of Large Language Models and Amazon Bedrock in Automated Medical Citation Retrieval and Clinical Data Synthesis.

The rapid proliferation of Large Language Models in healthcare presents unprecedented opportunities for clinical data synthesis and automated research assistance.

However, standard foundational models frequently suffer from a critical limitation known as hallucination, wherein they generate plausible but factually incorrect or entirely fabricated medical information. In clinical environments, where diagnostic precision and evidence-based practice are paramount, such unverified outputs pose severe risks to patient safety and data integrity. This paper proposes a robust architectural solution utilizing Amazon Bedrock to deploy an enterprise-grade artificial intelligence assistant integrated with a Retrieval-Augmented Generation framework. By decoupling the reasoning engine from its static training weights, the proposed system anchors all artificial intelligence responses to a curated repository of verified medical citations.

This methodology fundamentally shifts the paradigm from generative approximation to deterministic retrieval, significantly mitigating the occurrence of hallucination. The architecture systematically extracts peer-reviewed literature, cross-references clinical queries, and mandates the appending of verifiable citations, such as Digital Object Identifiers and primary source Uniform Resource Locators, to every output. Preliminary evaluations indicate that this programmatic intervention not only eradicates ungrounded assertions but also provides scalable, highly accurate data synthesis tailored for healthcare practitioners. Ultimately, this framework establishes a verifiable chain of clinical evidence, bridging the gap between advanced natural language processing capabilities and the stringent epistemological demands of modern medical science.