Large Language Models Assist in Tumor MDT

NCT07504367 | Recruiting

• 1 other identifier: SYSKY-2026-071-02
• Study Type: interventional
• Target: 60
• Locations: 1 country
• Sites: 2

Brief 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

Outcome Measures

Primary Outcomes (1)

• The overall score of the MDT report

  Clinical experts comprehensively evaluated the diagnosis and treatment opinions of different departments in the MDT report, and used the standardized Likert scale to comprehensively score the MDT reports of the intervention group and the control group (1 to 5 points, the higher the better).

  Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).

Secondary Outcomes (5)

• The radiation oncology score of the MDT report

  Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).

• The medical oncology score of the MDT report

  Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).

• The pathology score of the MDT report

  Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).

• The radiology score of the MDT report

  Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).

• The time consumption in writing an MDT report

  Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).

Study Arms (2)

AI-MDT

EXPERIMENTAL

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.

Other: LLM assists in MDT report writing

Trad-MDT

NO INTERVENTION

The control group used traditional information retrieval methods (such as Google, literature, and textbooks) to write MDT diagnosis and treatment opinions.

Interventions

LLM assists in MDT report writing OTHER

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.

AI-MDT

Eligibility Criteria

• Age: 25 Years - 33 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• A junior doctor with a practicing physician qualification certificate.
• Oncologists, surgeons, radiation oncologists, radiologists and pathologists with 3 to 5 years of clinical experience.
• Age: 25 to 33 years old, gender not limited.
• During the research period, one can participate for no less than 10 hours.
• Agree to participate in this research and sign the informed consent form.

You may not qualify if:

• Have participated in the previous diagnosis and treatment of any one of the 20 cases included in the study.

Sponsors & Collaborators

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

Study Sites (2)

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

Guangzhou, Guangdong, 510000, China

RECRUITING

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

Guangzhou, Guangdong, China

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms; Breast Neoplasms; Colorectal Neoplasms; Stomach Neoplasms; Liver Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases; Stomach Diseases; Liver Diseases

Study Officials

• Yunfang Yu, PhD

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

  STUDY CHAIR

• Herui Yao, PhD

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

  STUDY DIRECTOR

Central Study Contacts

Yunfang Yu, PhD

CONTACT

+8613660238987; yuyf9@mail.sysu.edu.cn

Herui Yao, PhD

CONTACT

+8613500018020; yaoherui@mail.sysu.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 11, 2026
• First Posted: March 31, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: March 31, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)