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Related Experiment Video

Updated: May 21, 2026

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

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

Published on: December 6, 2024

Precision oncology: from large language models to multi-agent systems.

Xiaotong Guo^1,2,3, Jun Chen⁴, Yuye Zhang⁵

¹Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Frontiers in Oncology

|May 20, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) advance precision oncology by integrating diverse data for cancer care. Multi-agent systems offer enhanced clinical decision support, guiding AI tool selection based on task complexity.

Keywords:

AI AI agent large language model multi-agent system oncology

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

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence in Oncology
Precision Medicine
Clinical Decision Support Systems

Background:

Growing volumes of electronic health records, medical imaging, and omics data necessitate advanced computational approaches in precision oncology.
Large language models (LLMs) and multimodal AI show promise for integrating complex health data and supporting clinical decisions.
Current single-model AI approaches face limitations in clinical reasoning, traceability, and workflow integration.

Purpose of the Study:

To review the applications of LLMs and multimodal AI across the precision oncology continuum, from screening to documentation.
To explore the emerging role of AI agents and multi-agent systems (MAS) in addressing limitations of single-model approaches.
To propose a task-architecture alignment framework for selecting appropriate AI systems in precision oncology.

Main Methods:

Structured narrative review of current AI technologies in precision oncology.
Analysis of LLM and multimodal AI applications in cancer screening, diagnosis, staging, treatment recommendation, and documentation.
Exploration of AI agents and MAS for advanced clinical decision support and workflow integration.

Main Results:

LLMs and multimodal AI are increasingly applied across the precision oncology care pathway, demonstrating significant potential.
Single-model AI systems exhibit limitations in complex clinical reasoning and integration.
AI agents and MAS represent a promising direction for more sophisticated and integrated precision oncology solutions.

Conclusions:

A task-architecture alignment framework is proposed to guide the selection of foundation models, single-agent, and multi-agent systems based on clinical task complexity and risk.
This framework aims to facilitate the design, evaluation, and clinical translation of AI systems in precision oncology.
The judicious application of AI, guided by task-specific needs, is crucial for advancing precision oncology.