Large Language Models Assist in Tumor MDT
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: LLM assists in MDT report writing.
- Who it may be relevant to
- Registry conditions: Lung Cancer, Breast Cancer, Colorectal Cancer, Stomach Cancer. Basic parameters: 25 years — 33 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Evaluating Large Language Models as Decision Support Agents in Pan-Cancer Tumor Boards: A Randomized Controlled Trial
Overview
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.
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 traditiona
Primary outcome measures
- The overall score of the MDT report [Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
Secondary outcome measures (5)
- The radiation oncology score of the MDT report [Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
- The medical oncology score of the MDT report [Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
- The pathology score of the MDT report [Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
- The radiology score of the MDT report [Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
- The time consumption in writing an MDT report [Time frame: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
- Have participated in the previous diagnosis and treatment of any one of the 20 cases included in the study.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Treatment
Study locations
China · 2 centers
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University — Guangzhou
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University — Guangzhou
Identifiers
NCT: NCT07504367 · SYSKY-2026-071-02