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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Will large language models transform clinical prediction?

Yusuf Yildiz¹, Goran Nenadic², Meghna Jani³

¹Faculty of Biology, Medicine and Health, School of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK. yusuf.yildiz@postgrad.manchester.ac.uk.

Diagnostic and Prognostic Research

|November 6, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) show potential for improving clinical prediction models using electronic health records. However, challenges in methodology, validation, infrastructure, and regulation must be addressed for effective healthcare integration.

Area of Science:

Artificial Intelligence in Medicine
Health Informatics
Clinical Decision Support

Background:

Large language models (LLMs) are gaining traction in healthcare applications.

Keywords:

Bias Clinical prediction models Fairness Large language models Regulation and reporting

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Clinical prediction models (CPMs) are crucial for diagnosis and prognosis.

Electronic health records (EHRs) contain rich longitudinal patient data.