Идёт набор NCT07504367

Large Language Models Assist in Tumor MDT

Без фазы С лечением Lung Cancer Breast Cancer Colorectal Cancer Stomach Cancer

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают: В протоколе указаны: LLM assists in MDT report writing.
Кому может быть актуально: Состояния в реестре: Lung Cancer, Breast Cancer, Colorectal Cancer, Stomach Cancer. Базовые параметры: 25 лет — 33 лет · Все.
Что важно проверить: Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится: Китай
Следующий шаг: Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →

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Официальное название

Evaluating Large Language Models as Decision Support Agents in Pan-Cancer Tumor Boards: A Randomized Controlled Trial

Обзор

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.

Вмешательства

Другое 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

Первичные конечные точки

The overall score of the MDT report [Срок оценки: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]

Вторичные конечные точки (5)

The radiation oncology score of the MDT report [Срок оценки: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
The medical oncology score of the MDT report [Срок оценки: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
The pathology score of the MDT report [Срок оценки: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
The radiology score of the MDT report [Срок оценки: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]
The time consumption in writing an MDT report [Срок оценки: Up to 4 weeks, complete the writing of medical opinions for all cases (n=20).]

Критерии участия

Критерии включения

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.