Back to search

Article

Improved precision oncology question-answering using agentic LLM

2024-09-24

Abstract excerpt

<h4>ABSTRACT</h4> The clinical adoption of Large Language Models (LLMs) in biomedical research has been limited by concerns regarding the quality, accuracy, and reliability of their outputs, particularly in precision oncology, where clinical decision-making demands high precision. Current models, often based on fine-tuned foundational LLMs, are prone to issues such as hallucinations, incoherent reasoning, and loss...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
6a2e91bf-e031-56a4-9c88-3083257dfe8a
DOI
10.1101/2024.09.20.24314076
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Improved precision oncology question-answering using agentic LLMDOI 10.1101/2024.09.20.24314076
Select a neighboring publication to make it the new centre.