Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought

Abstract

Our study highlights the applicability of enhancing the capabilities of foundation LLMs in medical domain-specific tasks, i.e., automated quantitative analysis of EHRs, addressing the challenges of labor-intensive and often manually conducted quantitative assessment of stroke in clinical practice and research. This approach offers a practical and accessible GAPrompt paradigm for researchers and industry practitioners seeking to leverage the power of LLMs in domain-specific applications. Its utility extends beyond the medical domain, applicable to a wide range of fields.

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