Recruiting nowNot phasedRandomised
What this trial is trying to do
The researchers are testing whether a large language model (LLM)—software trained to process and generate text—can help junior doctors write multidisciplinary team (MDT) cancer reports more accurately and efficiently. Doctors will either use an LLM or traditional information-retrieval methods when preparing reports.
The comparison asks whether AI assistance improves the experts’ overall score of the reports, with report-writing efficiency also considered, rather than changing treatment itself. Any improvement measured here would be in the quality and speed of documentation, not in tumour control or survival.
Worth knowing: This is a small, open-label study assessing reports produced by doctors, so it does not show whether AI assistance improves patient outcomes or treatment decisions.
Written from the trial's own registry entry to explain what the researchers are testing and why. Nothing here is a result — this trial has not reported one — and it is not a view on whether the treatment works or on whether it would suit you.
| Registry number | NCT07504367 |
|---|---|
| Recruitment | Recruiting now |
| Phase | Not phased |
| Design | randomised, blinded |
| Taking part | 60 people (planned) |
| Who can join | aged 25 Years to 33 Years |
| Run by | Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University |
| Started | |
| Main results expected | |
| Record last updated |
What is being tested
- Other LLM assists in MDT report writing
What it is measuring
The overall score of the MDT report
This is the trial's main question — the one it is designed and sized to answer. Anything else it reports is a secondary finding, and secondary findings are far more likely to be chance.
Not phased. Not a drug-phase trial. Often a device, a procedure, or a change in how care is delivered.
How the researchers describe it
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.
Written by the trial’s sponsor, quoted from its ClinicalTrials.gov record.
What this trial is looking for
- Any genotype
Read automatically from the criteria below, to make the list searchable. It is a summary of what the text mentions, not a decision about whether you qualify — and where the two disagree, the criteria are right and this is wrong.
Who the trial is looking for
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.
This is an extract. Whether you qualify is decided by the trial team against the full criteria, not by reading this page. Read the full eligibility criteria
Where it is running
Running at 2 sites. Listed in: China.
Individual hospitals, and whether each is currently open, are listed on the registry record. Sites open and close throughout a trial.
Worth asking your oncology team
Being listed here is not a recommendation, and no one page can tell you whether a trial is right for you. These are the questions it raises.
- Is this trial open at a hospital I could realistically travel to?
- Given my stage, my previous treatment and my tumour's molecular profile, would I be eligible?
- What would I be giving up by joining — is the comparison arm the treatment I would otherwise be having?
- What is already known about the safety of what is being tested?
The source
Registry record NCT07504367, registered by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University on ClinicalTrials.gov, a public database run by the US National Library of Medicine. The details on this page come from that record and are only ever as current as the sponsor has kept it.
This is a listing of a registered clinical trial, reproduced for information. It is not a recommendation, this site has no connection to the trial or its sponsor, and being listed here says nothing about whether the treatment works. Talk to your own oncology team before pursuing any trial.
This article summarises published research for general information. It is not medical advice, and it is not a substitute for a conversation with your own oncology team, who know your case. Do not start, stop, or change any treatment or supplement on the basis of what you read here.