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Updated: Jan 29, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Improving medication error classification using a reasoning large language model.
Anders Krifors1,2, Theodor Beskow3, Magnus Jonsson3
1Centre for Clinical Research Västmanland, Uppsala University, Västerås Hospital, 721 89 Västerås, Sweden.
A large language model (LLM) demonstrated expert-level performance in identifying medication errors from medical reports, achieving 96% concordance with expert classification. This AI tool can enhance patient safety by improving error detection efficiency and accuracy.
Area of Science:
- Artificial Intelligence in Healthcare
- Clinical Informatics
- Patient Safety Research
Background:
- Medication errors pose a significant threat to patient safety.
- Automated methods for identifying medication errors in incident reports are needed to improve efficiency and accuracy.
- Large Language Models (LLMs) show potential for analyzing complex medical text.
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