Comparison of Large Language Models and Expert Multidisciplinary Team Decisions in Colorectal Cancer
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- Agreement Between AI-Generated and MDT Treatment Decisions
研究概览
简要总结
The goal of this observational study is to evaluate the decision-making consistency between large language models (LLMs) and expert multidisciplinary teams (MDTs) in adult patients diagnosed with colorectal cancer who underwent MDT consultation between January 2023 and December 2024.
The main questions it aims to answer are:
How consistent are the treatment decisions generated by LLMs compared to actual MDT decisions? Do different LLMs (e.g., ChatGPT, DeepSeek) show varying levels of agreement with expert recommendations? What clinical factors contribute to differences between AI-generated and human expert decisions? Researchers will compare the AI-generated treatment recommendations with real-world MDT decisions using anonymized patient records to see if LLMs can reliably support clinical decision-making in oncology.
Participants will:
Have their de-identified clinical data (e.g., imaging, pathology, MDT notes) processed through several LLMs Not be contacted or receive any interventions, as this is a retrospective study using existing clinical records only.
详细描述
This is a retrospective, non-interventional observational study aiming to evaluate the consistency between treatment decisions made by large language models (LLMs) and multidisciplinary team (MDT) experts in the management of colorectal cancer (CRC).
Colorectal cancer is a highly heterogeneous malignancy requiring personalized treatment strategies, often developed through MDT discussions that integrate input from surgery, oncology, radiology, pathology, and other specialties. While MDTs improve treatment planning and outcomes, they are time- and resource-intensive, and subject to variability in expert judgment. With the rise of artificial intelligence, especially LLMs such as ChatGPT and DeepSeek, there is growing interest in their potential role in assisting or standardizing clinical decision-making.
In this study, researchers will retrospectively analyze de-identified clinical records of approximately 1,500 patients with histologically confirmed colorectal cancer who underwent MDT consultation at a tertiary cancer center between January 2023 and December 2024. Key clinical data-including demographic information, imaging reports (CT, MRI), endoscopy results, pathology findings, and MDT recommendations-will be extracted and anonymized.
These de-identified records will be input into several LLMs (ChatGPT, DeepSeek, Baichuan, and Qwen) running on secure offline servers. The models will be asked to generate treatment recommendations, which will be categorized into predefined decision codes (e.g., surgery, systemic therapy, chemoradiotherapy, further diagnostics). Each case will be input three times to assess the consistency of the model output.
The primary outcome is the agreement between AI-generated recommendations and original MDT decisions, quantified using Cohen's Kappa. Secondary analyses include comparison among LLMs using chi-squared tests, evaluation of output consistency via Fleiss' Kappa, and identification of clinical factors associated with discordant decisions.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients with a histologically confirmed diagnosis of colorectal cancer
- •Patients who received multidisciplinary team (MDT) consultation at Peking University Cancer Hospital between January 1, 2023 and December 31, 2024
- •Availability of complete clinical records, including(MDT consultation notes, CT or MRI imaging reports, Pathology reports, Outpatient or inpatient medical summaries)
排除标准
- •Incomplete or missing medical records related to MDT decision-making
- •MDT consultations conducted for non-oncologic purposes (e.g., hernia evaluation, stoma planning)
- •Missing critical clinical data such as imaging or pathology reports
- •Duplicate or conflicting records that prevent reliable data analysis
结局指标
主要结局
Agreement Between AI-Generated and MDT Treatment Decisions
时间窗: January 1, 2023 to December 31, 2024 (based on MDT consultation date)
Description: The primary outcome is the consistency between treatment recommendations generated by large language models (LLMs) and those made by expert multidisciplinary teams (MDTs) for colorectal cancer cases. Consistency will be quantified using Cohen's Kappa coefficient. Higher Kappa values indicate stronger agreement
次要结局
- Comparison of Agreement Across Different AI Models(January 1, 2023 to December 31, 2024)
- Output Stability of AI Models on Repeated InputsDescription(January 1, 2023 to December 31, 2024)
- Identification of Clinical Factors Associated With Decision Discordance(January 1, 2023 to December 31, 2024)