Large Language Models Assist in Tumor MDT

Summary

Multidisciplinary teams (MDTs) represent the gold standard for personalized tumor treatment, but they are limited by medical resources and accessibility Limitation. Although large language models (LLMs) have shown promise in medical reasoning, their multidisciplinary practicality in pan-cancer MDTs has not been fully explored. In the early stage of this project, LLMs with high clinical application efficacy were identified through benchmark tests, and an open-label randomized controlled study (RCT) was conducted based on these LLMs. The research aims to explore whether AI-assisted assistance can enhance the accuracy and writing efficiency of MDT diagnosis and treatment reports. This study intends to prospectively collect the diagnosis and treatment information of 20 patients and MDT diagnosis and treatment information. It is planned to recruit 40 junior doctors. Doctors in the intervention group will use LLM to assist in the writing of MDT reports, while doctors in the control group will use traditional information retrieval methods for the writing of MDT reports. Three clinical experts ultimately used a standardized Likert scale to conduct comprehensive and multidisciplinary scoring of the MDT reports of the intervention group and the control group. This study quantitatively compared the diagnosis and treatment quality and efficiency of the MDT AI-assisted model and the traditional model to verify the application potential of large language models in assisting tumor diagnosis and treatment.

Conditions

• Lung Cancer
• Breast Cancer
• Colorectal Cancer
• Stomach Cancer
• Liver Cancer

Interventions

OTHER

LLM assists in MDT report writing

This study was a prospective RCT, and the intervention content was an auxiliary tool for writing MDT reports. The intervention group used LLM to assist in the writing of MDT reports. The prescribed MDT medical records (excluding diagnosis and treatment opinions) were input into the LLM, and the output content could be used as a reference for the MDT report. Finally, the MDT diagnosis and treatment opinions were written under the personal judgment of the doctors. The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  lead OTHER

Principal Investigators

• Yunfang Yu, PhD · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

• Herui Yao, PhD · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

25 Years

Max Age

33 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-01-01

Primary Completion

2026-12-31

Completion

2026-12-31

Countries

• China

Study Locations