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Related Experiment Video

Updated: Jul 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

From Zero-Shot to Bedside: A Practical Playbook for Adapting Open-Source Large Language Models to Clinical Symptom

Li-Ching Chen1,2,3, Travis Zack2,3,4, Divneet Mandair2

  • 1UC Berkeley.

Proceedings of Machine Learning Research
|July 9, 2026
PubMed
Summary

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This study offers a practical guide for fine-tuning large language models (LLMs) on clinical notes. An LLM-assisted workflow improved annotation accuracy and reduced expert review burden for pancreatic cancer patients.

Area of Science:

  • Clinical Natural Language Processing (NLP)
  • Artificial Intelligence in Medicine
  • Machine Learning for Healthcare

Background:

  • Large language models (LLMs) show promise for analyzing clinical notes, but practical guidance on adapting open-source models and ensuring annotation quality is scarce.
  • Fine-tuning LLMs on sensitive clinical data requires careful consideration of privacy and task-specific adaptation.

Purpose of the Study:

  • To provide a playbook for fine-tuning open-source LLMs on de-identified clinical notes for pancreatic cancer patients.
  • To evaluate different prompting strategies and compare open-source models with proprietary ones like GPT-4o.
  • To develop and assess an LLM-assisted adjudication workflow for improving annotation quality and reducing expert burden.

Main Methods:

  • Fine-tuning of open-source LLMs on de-identified clinical notes from pancreatic cancer patients (pre-diagnosis and on-treatment).
Keywords:
Clinical NLPData adjudicationData augmentationDomain adaptationLarge language models

Related Experiment Videos

Last Updated: Jul 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

  • Evaluation of various prompting strategies and disease-level vs. task-specific adaptation.
  • Implementation of an LLM-assisted adjudication workflow to flag conflicting predictions for expert review.
  • Assessment of machine-generated annotations to augment limited expert labels.

    • Main Results:

    • The LLM-assisted adjudication workflow effectively identified annotation errors, concentrating expert review on a small subset of notes and improving downstream model performance.
    • Using a balanced mix of synthetic and human data for training enhanced the performance of fine-tuned models.
    • Strategies were identified to improve accuracy and reduce the annotation burden when deploying LLMs in clinical settings.

    Conclusions:

    • This work offers practical strategies for adapting and deploying open-source LLMs in clinical NLP tasks, enhancing accuracy and efficiency.
    • The developed LLM-assisted adjudication workflow is a key innovation for managing annotation quality at scale.
    • The findings support privacy-preserving, site-adapted clinical NLP through effective LLM fine-tuning and data augmentation.