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Evaluating AI and Human Expert Decisions in Colorectal Cancer

NCT07045207 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2025-07-01

No results posted yet for this study

Summary

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.

Conditions

Interventions

OTHER

LLM-MDT

Leveraging large language models (LLMs) to Generate Multidisciplinary Team (MDT) Treatment Recommendations

Sponsors & Collaborators

  • Peking University Cancer Hospital & Institute

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-07-01
Primary Completion
2026-06-01
Completion
2026-06-01

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07045207 on ClinicalTrials.gov