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

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Published on: December 6, 2024

Toward Automating the Summarization of Cancer Pathology Reports Using Large Language Models to Improve Clinical

Yirong Liu¹, Jacob John¹, Sagnik Sarkar¹

¹Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL.

JCO Clinical Cancer Informatics

|April 8, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) generate concise pathology summaries, outperforming physician summaries in objective metrics and completeness. This demonstrates LLMs

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Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Physicians face challenges integrating complex pathology data from multiple sources under time constraints.
Large language models (LLMs) offer potential for summarizing complex pathology findings.

Purpose of the Study:

To evaluate the performance of open-source LLMs in generating pathology summaries.
To compare LLM-generated summaries with physician-generated summaries for accuracy and completeness.

Main Methods:

Six LLMs (Llama 3.0, 3.1, 3.2, Mistral, Gemma, DeepSeek-R1) summarized original pathology reports from 94 thoracic clinic cases.
Summaries were objectively and subjectively evaluated against original reports and physician summaries.
Llama 3.1 underwent additional multi-evaluator subjective assessment.

Main Results:

LLM summaries significantly outperformed physician summaries in objective evaluation metrics (P < .0001).
DeepSeek, Mistral, Llama 3.1, and Llama 3.2 showed superior completeness in subjective evaluations (P < .0001).
LLM summaries maintained comparable correctness to physician summaries (P = 1.000).

Conclusions:

LLM-generated pathology summaries show superior objective performance and completeness compared to physician summaries.
LLMs show promise in improving clinical documentation and workflow efficiency in oncology.