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

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Biomedical Natural Language Processing in the Era of Large Language Models.

Naoto Usuyama¹, Cliff Wong¹, Sheng Zhang¹

¹Microsoft Research, Redmond, Washington, USA;

Annual Review of Biomedical Data Science

|April 17, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) are revolutionizing biomedical natural language processing (NLP), enhancing patient care and discovery. Addressing challenges like hallucinations and safety is key to unlocking AI

Keywords:

biomedical NLP generative AI large language models learning health system precision health real-world evidence

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Area of Science:

Biomedical informatics
Artificial intelligence
Natural language processing

Background:

Biomedicine has undergone significant digitization, from genomic sequencing to electronic medical records.
Large language models (LLMs) are now driving a generative artificial intelligence (AI) revolution in natural language processing (NLP).

Purpose of the Study:

To review the challenges and opportunities in biomedical NLP.
To provide historical context and survey the current state of the art.
To explore future frontiers for AI researchers and biomedical practitioners.

Main Methods:

Review of current literature and emerging trends in biomedical NLP.
Analysis of the impact of LLMs on healthcare and biomedical discovery.
Discussion of challenges including hallucinations, omissions, compliance, and safety.

Main Results:

Biomedical NLP automates tasks like knowledge extraction and medical abstraction, boosting productivity.
Emerging AI approaches promise creative gains and uncovering new capabilities using large-scale data.
Integrating diverse data modalities like imaging and genomics is crucial for comprehensive solutions.

Conclusions:

LLMs offer unprecedented possibilities for optimizing patient care and accelerating biomedical discovery.
Ensuring LLM compliance, safety, and addressing limitations like hallucinations are critical.
The integration of AI, particularly LLMs, is poised to transform biomedical research and practice.